For WizardKim-DPT2
Motivation to use big data and big
data analytics in external auditing
Lina Dagilien_e and Lina Klovien_e
Kauno Technologijos Universitetas, Kaunas, Lithuania
Abstract
Purpose – This paper aims to explore organisational intentions to use Big Data and Big Data Analytics
(BDA) in external auditing. This study conceptualises different contingent motivating factors based on prior
literature and the views of auditors, business clients and regulators regarding the external auditing practices
and BDA.
Design/methodology/approach – Using the contingency theory approach, a literature review and 21 in-
depth interviews with three different types of respondents, the authors explore factors motivating the use of
BDA in external auditing.
Findings – The study presents a few key findings regarding the use of BD and BDA in external auditing.
By disclosing a comprehensive view of current practices, the authors identify two groups of motivating
factors (company-related and institutional) and the circumstances in which to use BDA, which will lead to the
desired outcomes of audit companies. In addition, the authors emphasise the relationship of audit companies,
business clients and regulators. The research indicates a trend whereby external auditors are likely to focus
on the procedures not only to satisfy regulatory requirements but also to provide more value for business
clients; hence, BDAmay be one of the solutions.
Research limitations/implications – The conclusions of this study are based on interview data
collected from 21 participants. There is a limited number of large companies in Lithuania that are open to co-
operation. Future studies may investigate the issues addressed in this study further by using different
research sites and a broader range of data.
Practical implications – Current practices and outcomes of using BD and BDA by different types of
respondents differ significantly. The authors wish to emphasise the need for audit companies to implement a
BD-driven approach and to customise their audit strategy to gain long-term efficiency. Furthermore, the most
challenging factors for using BDA emerged, namely, long-term audit agreements and the business clients’
sizes, structures and information systems.
Originality/value – The original contribution of this study lies in the empirical investigation of the
comprehensive state-of-the-art of BDA usage andmotivating factors in external auditing. Moreover, the study
examines the phenomenon of BD as one of the most recent and praised developments in the external auditing
context. Finally, a contingency-based theoretical framework has been proposed. In addition, the research also
makes a methodological contribution by using the approach of constructivist grounded theory for the
analysis of qualitative data.
Keywords Big data, Contingent factors, Big data analytics, External auditing
Paper type Research paper
1. Introduction
In the past several years, the technology of Big Data (BD) has gained remarkably in
popularity within a variety of sectors, ranging from business and government to scientific
and research fields (Ajana, 2015). The area of accounting and auditing is not an exception, as
companies are confronted by an unprecedented level of semi-structured and unstructured
The authors are pleased to acknowledge comments on earlier version of the paper from delegates at
38th EAA Congress, Glasgow, April 2015.
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Received 27 January 2018
Revised 5 July 2018
18 September 2018
21 November 2018
Accepted 13 December 2018
Managerial Auditing Journal
Vol. 34 No. 7, 2019
pp. 750-
782
© EmeraldPublishingLimited
0268-6902
DOI 10.1108/MAJ-01-2018-1
773
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0268-6902.htm
http://dx.doi.org/10.1108/MAJ-01-2018-1773
massive data, which companies have to use and manage to be innovative, effective and
competitive. On one hand, we can see excitement about BD emerging because of the
recognition of opportunities in various areas (Marshall et al., 2015; Verma and
Bhattacharyya, 2017; Vera-Baquero et al., 2015; Enget et al., 2017). On the other hand, the
concept of BD is still confused (for example, social media data or business data) (Connelly
et al., 2016; Harford, 2014) and quite vague in terms of the circumstances of use.
According to Wang and Cuthbertson (2015), the potentially important role played by BD
and Big Data Analytics (BDA) in innovative auditing practice is evident. Quite a few studies
have discussed and analysed broad areas of BD and BDA in external auditing by explaining
and providing a context for researchers, drawing their attention to it in terms of general
issues (Alles and Gray, 2016; Alles, 2015; Earley, 2015; Wang and Cuthbertson, 2015;
Arnaboldi et al., 2017; Connelly et al., 2016) and arguing that the use of BDA is appropriate
and valuable to ensure the audit quality (Dubey and Gunasekaran, 2015; Brown-Liburd
et al., 2015; Vasarhelyi et al., 2015). BDA may improve the efficiency and effectiveness of
financial statement audits (KPMG, 2017; Cao et al., 2015; Yoon et al., 2015; Gepp et al., 2018),
but additional competencies and technological capabilities are necessary to implement BDA
(KPMG, 2017; Enget et al., 2017; Dubey and Gunasekaran, 2015; Brown-Liburd et al., 2015;
Zhang et al., 2015; Appelbaum et al., 2017, 2018).
Nonetheless, auditing is lagging behind the other research streams in the use of valuable
BDA (Gepp et al., 2018). However, research on understanding the motives for using BDA is
limited, as current studies do not attempt to explain why audit companies should actually
use BDA. Hence, an external audit is analysed from two process points of view – the audit
process between the audit company and client, and the audit process between the audit
company and regulatory bodies. In fact, BD only became accessible recently through
powerful analytical tools, but there are no obvious institutional forces that use BD
information or to implement BDA at the corporate level. The problematisation proposed in
the paper is the result of a dialectical interrogation (Alvesson and Sandberg, 2011) of audit
companies, business clients and regulatory bodies and the domain of literature targeted to
challenge assumptions. The use of innovative analytical tools such as BDA may cause a
tension among audit companies, business clients and regulators. This aspect arises because
of interdependence in the auditing process.
The previous literature has stipulated several contingent factors (namely, company size,
strategic orientation, modern technologies and regulatory environment) that can strengthen
or pose challenges to the use of BDA in external auditing. We elaborate on different
operating factors, as underlying theoretical assumptions, relevant to consider their different
influences on different stages of financial auditing, including the actors in financial auditing.
Based on these assumptions, we raise the following research question:
RQ1. What factors influence the motivation to use BDA in external auditing and how
intensively are these factors expressed by audit companies, business clients and
regulators?
The main contributions of this paper are the following. To the best of our knowledge, we are
among the first to study the comprehensive state-of-the-art of BDA usage, the motivating
factors and the potential outcomes for audit companies empirically. We explain how
different institutional and company-related factors are expressed and influence the decision
of whether to use BDA in external auditing. In particular, we focus on the phenomenon of
BD in external auditing by observing the views of diverse participants (namely, audit
companies, audit clients and audit regulators). Prior literature that examined audit analytics
focussed mainly on single influencing factors without taking the entire contingency-based
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view into account. This study investigates the use of BDA in external auditing from the
perspective of contingency theory. In addition, the study also makes a methodological
contribution by introducing the use of the constructivist grounded theory approach within
the context of a novel research question, for which the existing literature and data are
generally lacking.
The paper is organised as follows. The literature review and the theoretical framework
pertaining to BDA use in an external auditing are presented in Section 2 of this paper.
Section 3 presents the methodology used, while Section 4 presents the results and the
findings from the interviews. The discussion and conclusion are presented in Section 5 of
this paper. Research limitations and further research directions are also provided.
2. Literature review and theoretical framework
2.1 Literature review of big data analytics in external auditing
During the past few years, researchers have produced an impressive amount of general
reviews, conceptual and research papers in an attempt to define the concept of BD and data
analytic tools. The 3Vs (volume, variety and velocity) are the three best-known defining
dimensions of BD. Laney introduced the 3Vs concept in a 2001 MetaGroup research
publication, 3D data management: Controlling data volume, variety and velocity. In much of
the business research, BD is seen as a new opportunity to enhance productivity, efficiency
and innovativeness in companies (Sheng et al., 2017; Verma and Bhattacharyya, 2017;
Connelly et al., 2016; Marshall et al., 2015; Vera-Baquero et al., 2015; Ajana, 2015).
Overall, the emergence of BD is both promising and challenging for social research, as
well as for the accounting and auditing areas, which are regarded as intrinsically data-
intensive. According to Warren et al. (2015), BD will have increasingly important
implications for accounting ecosystems in all senses, even as new types of data become
accessible, as will the inherent technological paradoxes of BD and corporate reporting
(Al-Htaybat and Alberti-Alhtaybat, 2017; Bhimani and Wilcocks, 2014) and new
performance indicators based on BD (Arnaboldi et al., 2017).
In general, auditors work with structured financial data; however, the volume and
complexity of business companies require even more rapid and sophisticated information
and analyses of unstructured or semi-structured non-financial BD from both internal and
external sources. In external auditing, BD may be conceptualised as an additional
information resource that has a direct effect on the understanding about the environment of
the business client and the performance of an audit. Moreover, the inclusion of BD may
contribute to the development and evolution of effective BDA tools and changes in the audit
processes.
BDA is the process of inspecting, cleaning, transforming and modelling BD to discover
and communicate useful information and patterns, suggest conclusions and support
decision-making (Cao et al., 2015) by using “smart” algorithms (Davenport, 2014). According
to Wang and Cuthbertson (2015), the potential of BDA to improve the practice of auditing is
quite significant. A detailed literature review is commonly accepted as the beginning step in
research and is important to indicate relevant research in a field. Accordingly, this research
began with a literature review of the fields of BD, BDA and auditing. Research synthesis
was selected as the method for the literature review with the aim of using the existing
literature (Cooper et al., 2009; Dixon-Woods et al., 2005). The literature review outlines a few
main directions and possible influences of BDA in the context of auditing. A major research
stream in the field argues that use of BDA is useful and valuable for ensuring audit quality
(Cao et al., 2015; Dubey and Gunasekaran, 2015; Brown-Liburd et al., 2015; Yoon et al., 2015;
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Vasarhelyi et al., 2015) by improving the efficiency and effectiveness of financial statement
audits and by using BD as audit evidence.
The second stream of research focusses on additional competences that are necessary to
ensure an effective process when using BDA (Dubey and Gunasekaran, 2015). The latest
research by McKinney et al. (2017); Enget et al. (2017); Janvrin and Weidenmier Watson
(2017) and Sledgianowski et al. (2017) emphasises the need to incorporate issues of BD and
BDA into the accounting curriculum by acknowledging that these technologies are
transforming the accounting profession (Enget et al., 2017; Fay and Negangard, 2017;
Brown-Liburd et al., 2015; Zhang et al., 2015).
The third stream of research emphasises the role of changes in auditing standards. On
one hand, Appelbaum et al. (2017) argued that the standards themselves do not forbid the
use of BDA, but that the economics of external audits make analytics more difficult or
nearly impossible to use. On the other hand, the key methodological problem is using BD as
audit evidence (Brown-Liburd and Vasarhelyi, 2015). According to the standards, BD
evidence should be considered as less reliable for audit evidence (Appelbaum, 2016). Hence,
changes in the methodological audit approach, a change in standards to focus on data, the
processes that generate them and the analysis thereof, changes in the nature of accounting
records and auditing domains will add value and relevance to the accounting profession
(KPMG, 2017; Krahel and Titera, 2015; Vasarhelyi et al., 2015; Gray and Debreceny, 2014).
Moreover, updated standards may help to overcome the auditing profession’s apparent
reluctance to engage with BDA (Gepp et al., 2018).
Finally, the fourth stream of research explains the technological challenges for
companies of using BDA, with the focus on continuous auditing technology (Rikhardssona
and Dull, 2016; Appelbaum et al., 2016; Sun et al., 2015; Chen et al., 2015; Alles, 2015; Chiu
et al., 2014) and BD techniques (Gepp et al., 2018; Appelbaum et al., 2017). Moreover,
according to the literature review, Appelbaum et al. (2018) classified the audit analytics used
in the various audit stages. As external auditing is inseparable from the characteristics of
business clients, Al-Htaybat and Alberti-Alhtaybat (2017) identified the inherent
technological paradoxes of using BD in corporate reporting.
According to the literature review, it could be stated that the main streams of research
focus on and disclose the outcomes and value of the use of BDA in external auditing, the
aspects that have an influence on the efficient use of BDA and discuss the interaction
between BD and traditional sources of data, as well as BD’s impact on audit judgement and
behavioural research. It could also be stated that the external conditions and the
environment have an influence on the use of BDA in external auditing. On the other hand,
the research could be described as fragmented, disclosing different but limited aspects that
motivate or challenge the use of BDA in external auditing and a complete list of motivation
factors influencing the use of BDA in external auditing has not been researched.
2.2 The theoretical framework
Contingency theory focusses on how elements must fit together to reach the desired
configuration and the forms of fit, as proposed by Venkatraman (1989). In fact,
the contingency-based approach that is used widely in management research (Chenhall, 2003;
Chapman, 1997; Ittner and Larcker, 1997) could be also applied to explain audit companies’
intentions to adopt analytical tools at the corporate level.
Considering the complexity and dynamism of the audit process, the necessity of using
BDA might be influenced by different, contingent, external and internal factors. Auditors
require access to documents, systems, policies and procedures to manage an audit. They
must remain compliant with accounting and auditing standards, government regulations
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and internal requests. Audit teams may begin the audit process with meetings during which
they gain risk and control awareness. Auditors perform substantive procedures and test
controls, and then draft reports that they submit to management and regulatory authorities
(Davoren, 2016). Many contingency variables have been found to be relevant, including the
environment – in particular, environmental uncertainty and market competition (Otley,
2016), technology (Otley, 1980, 2016; Chenhall, 2003), national culture (Ahmad and
Schroeder, 2003; Flynn and Saladin, 2006; Otley, 2016), strategic context (Wickramasinghe
and Alawattage, 2007; Sila, 2007) and company size and structure (Otley, 2016;
Wickramasinghe and Alawattage, 2007). While it is possible that all these play an important
role in the design of control systems (Brivot et al., 2017), this paper focusses particularly on
the main contingent factors that have been subject to investigation, namely, the
environment, technology, strategic context, size and structure. The contingency of natural
culture has not been taken into consideration.
Environment, as a contingency factor, may constitute the market and its associated
factors, such as prices, products, competition, government policies, etc., (Wickramasinghe
and Alawattage, 2007). Environment (as a contingency) may constitute the audit market’s
uncertainty and its associated factors, such as audit fees, competition and regulators’
policies, such as the attitudes of those setting the standards (Li et al., 2018). Looking at the
BDA’s influence from the external auditing point of view, audit market regulators play a
particularly important role in ensuring audit companies’ public quality aspects and
enhancing the use of data analytic tools.
Technologies can be understood as the processes used by companies to convert inputs
into outputs (Khandwalla, 1977). When a company fails to match its technology to its
structure, it does not succeed as a sustained organisation (Wickramasinghe and Alawattage,
2007). In audit companies, technologies involve both knowledge and techniques. Moreover,
technology, as a contingent factor, refers to the so-called hard IT-related aspects adopted by
companies (Garengo and Bititci, 2007). Hence, BDA, as an IT tool, may have a direct impact
on the audit process by influencing the audit phase of engagement. BDA may have an
indirect impact on the audit planning phase, as audit strategies and audit plans are
developed according to the data and information coming from the analysis of client’s
environment. BDA, as an IT tool, may also have a direct influence on compliance and
substantive testing and on evaluations and reports. Overall, the need to use BDA may
depend on the requirements of the audit regulatory bodies and business clients and on
internal technological capabilities, IT-related managerial activities, such as the internal
investments in hardware and software, external consultants, etc., (Tarek et al., 2017).
Based on the notions of contingency theory, researchers have discussed how the fit
between environment and strategy can influence organisational performance. Thompson
(1967) argued that changes in technology and environmental factors resulted in differences
in structures, strategies and decision processes. Henderson and Mitchell (1997), Spanos and
Lioukas (2001) and Johnson and Scholes’ (2008) research results supported the argument
that strategy was one of the effects that had influence as a significant determinant of
performance. Pateli and Giaglis (2005) developed a structured approach to changing the
business model of a company (including strategy perspective), which introduced a
technological innovation by keeping the principles of the old (traditional) business logic and
taking the effects incurred from the firm’s internal and external environment into account.
With reference to contingency theory, it might be suggested that strategic orientation could
have a significant influence in persuading audit companies to use BDA in auditing process
in an attempt to find the fit among new trends in technology, the environment and
organisational strategy. Referring to contingency theory, one might suggest that strategic
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orientation could influence audit companies to use BDA in auditing process significantly. A
BD-based approach is inseparable from the corporate core strategy and aims. As suggested
by Gepp et al. (2018), long-term orientation towards the use of BDA may outline future
opportunities for auditing in the context of real-time information and on collaborative
platforms and in peer-to-peer marketplaces.
Size has also been found to be an important contingent factor in understanding the
nature of organisational structures and behaviour (Wickramasinghe and Alawattage, 2007;
Otley, 2016). This implies that audit companies need to pay attention to the size of the audit
company itself and to that of the business client when creating an audit strategy and plan.
According to contingency theory, large companies have extensive specialisation,
standardisation and formalisation, but these features are less important in small companies
(Wickramasinghe andAlawattage, 2007); thus, it could be stated that small clients might not
be able to provide all the necessary information as BD for further analysis and the
application of BDA tools. Furthermore, small audit companies might not be able to use BDA
for their business clients because of a lack of trained staff and limited technological
capabilities.
Structure refers to the establishment of certain relationships among people with specified
goals and tasks (Wickramasinghe and Alawattage, 2007). A poorly fitting structure is
nothing else but a waste of resources and leads to the ultimate collapse of the business
(Mintzberg, 1987; Otley, 2016). Accordingly, it could be stated that different methods,
instruments, functions and processes cannot be designed without finding the best structure
alignment. From a BDA point of view, it might be assumed that a suitable and organic
structure would be able to support the implementation of innovative analytical tools in audit
companies and vice versa.
The literature describes several factors that can strengthen or pose a challenge to the use
of BDA in external auditing by integrating them in a theoretical framework (Figure 1).
The theoretical framework contains key participants involved in the auditing process
(audit companies, business clients and regulators), the auditing process (where BDA might
appear in different phases of an audit) and the contingent factors discussed above.
3. Research methodology
Based on the literature review, we explored different contingent factors that may motivate
the use of BD and BDA in external auditing theoretically. Qualitative research (Birkinshaw
Figure 1.
Theoretical
framework for
influencing factors to
use BDA in external
auditing
Process
Influence
REGULATORY BODIES
AUDIT COMPANY
AUDIT PROCESS
BUSINESS CLIENT COMPANY
Contingent
factors:
Environment
Technology
Company size
Strategic
orientation
Structure
BD/A
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et al., 2011) adopted the constructivist grounded theory approach as described by Charmaz
(2006, 2014) for twomain reasons:
(1) BD and BDA are rarely researched phenomena within the field of auditing, and we
were motivated to understand “the actual production of meanings and concepts
used by social actors in real settings” (Gephart, 2004, p. 457).
(2) We aimed to develop theoretical insights into a process about which there is little
extant theorising or empirical knowledge (Suddaby, 2006).
This research uses the analysis approach suggested by Corbin and Strauss (1990) to present
rich and detailed descriptions, which allows the reader to make sufficient contextual
judgements to transfer the interview findings to alternative settings.
We followed the main stages in grounded theory research for qualitative data analysis
(McNabb, 2008; Corley, 2015), namely, collecting data, open coding, axial coding and
developing theoretical insights.
3.1 Data collection
The research on the motivation to use BDA in external audits was conducted using semi-
structured interviews to allow for follow-up questions. Interview questions derived from
theory are the tools used to obtain information that will help to answer the research question
(Glesne, 2006).
The respondents were selected on the basis of two considerations, namely, the company
and the respondent’s position. With regard to the first consideration, the companies that
were selected as the three case studies were selected an audit network company dealing with
DA, a business client company dealing with BD and a regulator. This selection was intended
to obtain different perspectives on the same phenomenon. Table I shows the description of
the sample.
For the second consideration, the respondents were selected according to their positions
in the company. Hence, the respondents were auditors and BD analysists working and
Table I.
Sample description
Cases/companies
Duration of recorded
interviews in minutes
Transcript
pages
No. of
interviews
Big 4 (1) 41.48 7 1
Big 4 (2) 43.04 8 1
Big 4 (3) 36.45 7 1
Big 4 (4) 42.32 7 1
International audit network 130.05 27 3
National audit network 47.37 11 1
Audit companies 340.71 67 8
Global financial services and IT company 105.53 24 5
Financial institution operating worldwide 90.41 20 2
National energy company 25.59 5 1
Business companies (clients) 221.53 49 8
Tax analytics 141.58 32 4
Audit controller 39.14 8 1
Regulators 180.72 40 5
Total 742.96 156 21
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dealing with the company’s data. The selection of the participants, as different stakeholders,
was also intended to improve the validity and reliability of the study (Yin, 2003) (Table II).
During the face-to-face interviews, which lasted for 35 min on average, the participants
were given a copy of the interview guide (questionnaire, see Appendix) to ensure sufficient
coverage of the research aim and the optimal use of time.
Part 1 was related to the background information and general understanding of BD in
the company and the motivating factors for using BDA. Part 2 was related to the practical
aspects of using BDA in the audit process. The proposed questions included “why” and
“how” information and the respondents were asked to discuss the reasons, motivations,
creation, implementation and use processes of BDA, including values, its challenges and the
possible changes for the auditing process.
The interviews were tape-recorded with prior permission from the participants after they
signed an official agreement. Towards the end of each interview, time was allowed for open
and informal discussions to extract information that participants might otherwise have been
reluctant to provide during the formal interview sessions. Overall, the interviews lasted for
12 h and 38 min, resulting in 156 pages of transcripts. The interviews were conducted in
Lithuanian or English. Data were collected and analysed in 2015-2017, except for the
interview with the BDA analyst from the audit company, which was conducted and
analysed in 2018.
3.2 The setting of the Lithuanian audit market
We focus next on the description of the setting of the Lithuanian audit market as a critical
factor for the analysis and interpretation of the data.
The Lithuanian audit market is relatively young and concentrated. In 2009, the National
Audit Standards were abandoned, and only the International Standards on Auditing (ISA)
have been applied since. According to the data from the Lithuanian Chamber of Auditors of
1 February 2017, 357 auditors and 170 audit companies have been certified, of which 141 out
of 170 audit companies were listed as very small companies, 25 audit companies as small
companies, 4 audit companies as medium companies and 1 audit company as large.
In 2015, Lithuanian audit companies conducted 4,217 audits in total, including 3,898
financial statement audits in Lithuania, 273 audits on consolidated financial statements in
Lithuania, 44 audits on interim financial statements in Lithuania and 2 audits abroad
(Lithuanian Chamber of Auditors Report, 2015). Among the clients of audit companies, the
current companies include public interest entities and companies that are legally required to
carry out audits but, in general, there are not many large clients.
The audit market in Lithuania is concentrated – the ten largest audit companies,
according to the received revenue from audit activities in 2015, accounted for almost 70 per
cent of the audit market. The average fee per audit performed in 2015 amounted to e414,304.
The highest average fee for one audit was for the companies in the Big 4 – e869,850, which is
four times higher than it was for audit companies with one or two auditors and three times
Table II.
Position of
respondents
Cases/companies
Auditors BD analytics
Senior Partner Field expert Head
Audit companies 4 3 � 1
Business client companies 1 � 3 4
Regulators 1 � � 4
Total 9 12
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higher than it was for audit companies with three or more auditors (Lithuanian Chamber of
Auditors Report, 2015). However, given the fact that the audit companies for the Big 4 spend
most of their time on audits, the difference in the average fee for the audit service is lower.
Significant fluctuations in the fees for services between international and smaller national
audit companies are typical of the Lithuanian audit market. This situation can also be
explained by the fact that international networking audit companies are auditing the largest
and, at the same time, the most complex business companies.
3.3 Coding and analyses
Preliminary coding on the basis of the 21 interviews was developed first. After the
transcription of all the interviews was completed, all the transcripts were analysed by both
researchers separately via a systematic process of coding and categorisation intended to
group the information from the transcripts into similar concepts or themes that emerged
from the analysis. We then discussed the open coding of sentences or paragraphs within the
transcripts to identify key concepts emerging from the data and to link them to what
allowed agreeing on certain open codes. Table III illustrates the open coding of the interview
transcripts.
During the process of our further discussions and analyses, open codes were assigned to
broader categories, called second-order codes, which highlighted the relationships among
the open codes (Lee, 1999). These second-order codes were then used to create broader
categories – axial codes – to facilitate theoretical insights (Lee, 1999), such as current
practices, company factors, institutional factors and outcomes. Table IV shows the axial
codes and the descriptions thereof.
Coding process and codes, as a method of qualitative data analysis, (McNabb, 2008;
Corley, 2015) allowed for the identification of key concepts emerging from the qualitative
data – the transcripts. Meaningful results and findings are presented on the basis of axial
codes, which indicated the main groups of motivating factors for and the circumstances in
which to use BD and BDA in external auditing.
4. Results and findings
After careful consideration of the second-order and axial codes, “Current Practices” was
organised to include the open codes of experience, benefits, financial resources and
increasing trend, which were identified as having similarities based on their currently
existing features. During the data analysis process, the second-order and axial code
“institutional factors” was organised using open codes such as regulation system, market
structure and education. Three open codes, namely, strategic decisions, governance
structure and size were identified as a second-order code strategy-related factors and three
open codes, namely, information system (IS), competent teams and internal capabilities were
identified as a second-order code, “resource-related factors”. These two second-order
codes
were then used to create a broader category, namely, the axial code “company factors”.
There were three open codes, which were planning, management and reporting, which were
integrated based on their properties in a second-order code, “internal control”. Five open
codes were understanding the client’s company, audit planning, audit performance and
conclusion and audit team and audit fee were identified as having similarities; thus, they
were combined in a second-order code, “audit process”. In addition, the open codes audit
quality and control of audit quality were combined in a second-order code, “quality”. These
three second-order codes were identified as having similarities, in the main areas that are
influenced by the use of BD/BDA in business and audit companies and were combined in an
axial code, “outcomes”.
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ra
te
gy
an
d
to
p
m
an
ag
em
en
t’s
at
tit
ud
e/
co
m
m
itm
en
tt
o
us
in
g
B
D
an
d
m
od
er
n
da
ta
an
al
yt
ic
to
ol
s
H
ig
h
im
po
rt
an
ce
c
H
ig
h
im
po
rt
an
ce
N
ot
di
sc
lo
se
db
G
ov
er
na
nc
e
st
ru
ct
ur
e
In
fo
rm
at
io
n
re
la
te
d
to
to
p
m
an
ag
em
en
t-
go
ve
rn
m
en
t,
fo
re
ig
n
m
an
ag
em
en
t,
na
tio
na
l
sh
ar
eh
ol
de
rs
,g
lo
ba
ln
et
w
or
ki
ng
co
m
pa
ny
H
ig
h
im
po
rt
an
ce
H
ig
h
im
po
rt
an
ce
N
ot
di
sc
lo
se
d
IS
In
fo
rm
at
io
n
re
la
te
d
to
th
e
ov
er
al
lc
or
po
ra
te
in
fo
rm
at
io
n
sy
st
em
,i
nc
lu
di
ng
th
e
in
te
rn
al
co
nt
ro
ls
ys
te
m
,fi
na
nc
ia
la
cc
ou
nt
in
g
pr
og
ra
m
m
es
an
d
no
n-
fi
na
nc
ia
ld
at
a
pr
og
ra
m
m
es
,d
at
ab
as
es
an
d
so
ft
w
ar
e
us
ed
,
le
ve
lo
fc
om
pu
te
ri
sa
tio
n
of
bu
si
ne
ss
pr
oc
es
se
s
D
is
cl
os
ed
D
is
cl
os
ed
D
is
cl
os
ed
B
en
efi
ts
In
fo
rm
at
io
n
re
la
te
d
to
th
e
be
ne
fi
ts
of
B
D
A
,
in
cl
ud
in
g
ad
va
nt
ag
es
re
ce
iv
ed
,t
im
e
ef
fi
ci
en
cy
,m
on
ey
sa
vi
ng
s
an
d
va
lu
e
fo
r
so
ci
et
y
by
pr
ov
id
in
g
da
ta
th
at
ar
e
m
or
e
re
lia
bl
e
D
is
cl
os
ed
D
is
cl
os
ed
D
is
cl
os
ed
Fi
na
nc
ia
l
re
so
ur
ce
s
In
fo
rm
at
io
n
re
la
te
d
to
co
st
s
of
cr
ea
tin
g
an
d
im
pl
em
en
tin
g
B
D
A
,i
nc
lu
di
ng
th
e
fi
na
nc
ia
l
re
so
ur
ce
s
ne
ed
ed
D
is
cl
os
ed
D
is
cl
os
ed
D
is
cl
os
ed
w
ith
an
or
ie
nt
at
io
n
to
w
ar
ds
th
e
fu
tu
re
Si
ze
In
fo
rm
at
io
n
re
la
te
d
to
th
e
co
nd
iti
on
s
ne
ed
ed
to
co
lle
ct
an
d
im
pl
em
en
tB
D
su
ch
as
th
e
au
di
tc
om
pa
ny
’s
si
ze
an
d
th
e
cl
ie
nt
’s
si
ze
H
ig
h
im
po
rt
an
ce
,a
ud
it
co
m
pa
ny
’s
si
ze
H
ig
h
im
po
rt
an
ce
,
cl
ie
nt
’s
si
ze
N
ot
di
sc
lo
se
d
(c
on
tin
ue
d)
Table III.
Open codes derived
from different
interview transcripts
Big data and
big data
analytics
759
O
pe
n
co
de
s
D
ef
in
iti
on
A
ud
it
co
m
pa
ni
es
B
us
in
es
s
co
m
pa
ni
es
T
ax
an
d
au
di
tr
eg
ul
at
or
s
Pl
an
ni
ng
In
fo
rm
at
io
n
re
la
te
d
to
th
e
de
ve
lo
pm
en
to
f
pl
an
ni
ng
an
d
fo
re
ca
st
in
g
pe
rf
or
m
an
ce
,
pr
oc
es
se
s
an
d
ac
tiv
iti
es
by
us
in
g
B
D
A
N
ot
di
sc
lo
se
d
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
U
nd
er
st
an
di
ng
th
e
cl
ie
nt
’s
co
m
pa
ny
In
fo
rm
at
io
n
re
la
te
d
to
un
de
rs
ta
nd
in
g
th
e
cl
ie
nt
’s
co
m
pa
ny
an
d
its
en
vi
ro
nm
en
t,
be
tt
er
ev
al
ua
tio
n
of
in
he
re
nt
ri
sk
s
an
d
th
e
co
nt
ro
lt
he
re
of
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
N
ot
di
sc
lo
se
d
A
ud
it
pl
an
ni
ng
In
fo
rm
at
io
n
re
la
te
d
to
th
e
pl
an
ni
ng
ac
tiv
iti
es
,p
re
pa
ra
tio
n
of
th
e
au
di
tp
la
n
an
d
au
di
tp
ro
gr
am
m
es
by
us
in
g
B
D
A
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
N
ot
di
sc
lo
se
d
A
ud
it
pe
rf
or
m
an
ce
an
d
co
nc
lu
si
on
In
fo
rm
at
io
n
re
la
te
d
to
pe
rf
or
m
in
g
th
e
au
di
t,
th
e
ap
pl
ic
at
io
n
of
an
al
yt
ic
al
pr
oc
ed
ur
es
an
d
co
nt
ro
lt
es
ts
,p
ro
vi
di
ng
th
e
au
di
to
r’s
op
in
io
n,
co
nc
lu
si
on
,c
on
tin
uo
us
au
di
tin
g
in
st
ea
d
of
on
a
sa
m
pl
e
ba
si
s
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
N
ot
di
sc
lo
se
d
R
ep
or
tin
g
In
fo
rm
at
io
n
re
la
te
d
to
pr
ov
id
in
g
re
su
lts
ab
ou
tt
he
co
m
pa
ny
in
th
e
re
po
rt
to
m
an
ag
em
en
t,
ex
te
rn
al
st
ak
eh
ol
de
rs
,a
nd
th
e
lik
e
D
is
cl
os
ed
,a
ud
it
co
nc
lu
si
on
D
is
cl
os
ed
,r
ep
or
t
to
m
an
ag
em
en
t
an
d
so
on
.
N
ot
di
sc
lo
se
d
A
ud
it
qu
al
ity
In
fo
rm
at
io
n
re
la
te
d
to
hi
gh
er
au
di
tq
ua
lit
y
by
em
pl
oy
in
g
B
D
A
an
d
an
al
ys
in
g/
ch
ec
ki
ng
10
0
pe
rc
en
to
fc
or
po
ra
te
da
ta
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
D
is
cl
os
ed
w
ith
an
or
ie
nt
at
io
n
to
w
ar
ds
th
e
fu
tu
re
Co
nt
ro
lo
fa
ud
it
qu
al
ity
In
fo
rm
at
io
n
re
la
te
d
to
th
e
co
nt
ro
lo
fa
ud
it
qu
al
ity
in
si
de
th
e
au
di
tc
om
pa
ny
,a
s
w
el
la
s
ex
te
rn
al
pu
bl
ic
co
nt
ro
l
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
D
is
cl
os
ed
w
ith
an
or
ie
nt
at
io
n
to
w
ar
ds
th
e
fu
tu
re
M
an
ag
em
en
t
In
fo
rm
at
io
n
re
la
te
d
to
im
pr
ov
em
en
ts
in
co
nt
ro
la
nd
de
ci
si
on
-m
ak
in
g
fu
nc
tio
ns
by
us
in
g
B
D
an
d
B
D
A
N
ot
di
sc
lo
se
d
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
(c
on
tin
ue
d)
Table III.
MAJ
34,7
760
O
pe
n
co
de
s
D
ef
in
iti
on
A
ud
it
co
m
pa
ni
es
B
us
in
es
s
co
m
pa
ni
es
T
ax
an
d
au
di
tr
eg
ul
at
or
s
A
ud
it
te
am
In
fo
rm
at
io
n
re
la
te
d
to
th
e
ef
fe
ct
iv
e
m
an
ag
em
en
to
ft
he
au
di
tt
ea
m
by
us
in
g
B
D
an
d
B
D
A
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
N
ot
di
sc
lo
se
d
A
ud
it
fe
e
In
fo
rm
at
io
n
re
la
tin
g
to
au
di
tp
ri
ce
s,
w
hi
ch
co
ul
d
be
m
or
e
co
m
pe
tit
iv
e
an
d
ea
si
ly
m
an
ag
ed
by
us
in
g
B
D
A
in
au
di
tc
om
pa
ni
es
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
N
ot
di
sc
lo
se
d
R
eg
ul
at
io
n
sy
st
em
In
fo
rm
at
io
n
re
la
te
d
to
th
e
na
tio
na
l
re
gu
la
tiv
e
bo
di
es
an
d
le
ga
la
ct
s
in
fl
ue
nc
e
on
th
e
us
e
of
B
D
D
is
cl
os
ed
as
ho
w
m
uc
h
th
e
au
di
tr
eg
ul
at
or
is
st
ri
ct
an
d
re
qu
ir
es
ad
di
tio
na
lr
el
ia
bi
lit
y
te
st
s,
an
al
yt
ic
al
pr
oc
ed
ur
es
,e
tc
.
D
is
cl
os
ed
,
be
ca
us
e
di
ff
er
en
t
se
ct
or
s
ha
ve
di
ff
er
en
t
re
gu
la
tio
ns
.
D
is
cl
os
ed
,b
y
di
sc
lo
si
ng
ho
w
m
uc
h
na
tio
na
lt
ax
re
gu
la
to
rr
eq
ui
re
s
on
lin
e
da
ta
,l
ev
el
of
ac
co
un
tin
g
co
m
pu
te
ri
za
tio
ns
M
ar
ke
ts
tr
uc
tu
re
In
fo
rm
at
io
n
re
la
te
d
to
th
e
m
ar
ke
ts
tr
uc
tu
re
(c
om
pe
tit
io
n,
ol
ig
op
ol
y
or
m
on
op
ol
y)
in
th
e
in
du
st
ry
(b
ot
h
th
e
au
di
tc
om
pa
ny
an
d
th
e
cl
ie
nt
),
th
e
in
fl
ue
nc
e
of
co
m
pe
tit
or
’s
on
th
e
de
ci
si
on
to
us
e
B
D
D
is
cl
os
ed
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
Co
m
pe
te
nt
te
am
In
fo
rm
at
io
n
re
la
te
d
to
th
e
co
m
pe
te
nt
au
di
t
te
am
,e
m
pl
oy
ee
s
an
d
co
m
pe
te
nc
e
ne
ed
ed
to
w
or
k
an
d
us
e/
an
al
ys
e
B
D
in
a
cl
ie
nt
’s
co
m
pa
ny
,b
ei
ng
ab
le
to
ap
pl
y
B
D
A
H
ig
h
im
po
rt
an
ce
H
ig
h
im
po
rt
an
ce
N
ot
di
sc
lo
se
d
(c
on
tin
ue
d)
Table III.
Big data and
big data
analytics
761
O
pe
n
co
de
s
D
ef
in
iti
on
A
ud
it
co
m
pa
ni
es
B
us
in
es
s
co
m
pa
ni
es
T
ax
an
d
au
di
tr
eg
ul
at
or
s
In
te
rn
al
ca
pa
bi
lit
ie
s
In
fo
rm
at
io
n
re
la
te
d
to
th
e
ac
tiv
iti
es
,
ca
pa
bi
lit
ie
s
an
d
in
te
rn
al
pr
oc
es
se
s
ne
ed
ed
to
pr
ep
ar
e
an
d
us
e/
an
al
ys
e
B
D
in
a
co
m
pa
ny
su
ch
as
IT
w
ith
re
ga
rd
to
in
fr
as
tr
uc
tu
re
D
is
cl
os
ed
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
In
cr
ea
si
ng
tr
en
d
In
fo
rm
at
io
n
re
la
te
d
to
th
e
in
cr
ea
si
ng
ro
le
an
d
in
fl
ue
nc
e
of
B
D
A
fo
rd
iff
er
en
tp
ur
po
se
s
in
co
m
pa
ni
es
gl
ob
al
ly
,a
s
w
el
la
s
po
lit
ic
al
de
ci
si
on
s
D
is
cl
os
ed
D
is
cl
os
ed
D
is
cl
os
ed
E
du
ca
tio
n
In
fo
rm
at
io
n
re
la
tin
g
to
th
e
in
cr
ea
si
ng
ne
ed
fo
rc
om
pe
te
nt
em
pl
oy
ee
s
w
ith
bu
si
ne
ss
,I
T
an
d
m
at
he
m
at
ic
al
co
m
pe
te
nc
e
gl
ob
al
ly
D
is
cl
os
ed
H
ig
h
im
po
rt
an
ce
D
is
cl
os
ed
N
ot
es
:a
D
is
cl
os
ed
m
ea
ns
th
at
th
e
op
en
co
de
w
as
m
en
tio
ne
d
an
d
di
sc
us
se
d
du
ri
ng
th
e
in
te
rv
ie
w
;b
no
td
is
cl
os
ed
m
ea
ns
th
at
th
e
op
en
co
de
w
as
no
tm
en
tio
ne
d
or
di
sc
us
se
d
du
ri
ng
th
e
in
te
rv
ie
w
;c
hi
gh
im
po
rt
an
ce
m
ea
ns
th
at
th
e
op
en
co
de
w
as
m
en
tio
ne
d
an
d
di
sc
us
se
d
ve
ry
st
ro
ng
ly
du
ri
ng
th
e
in
te
rv
ie
w
Table III.
MAJ
34,7
762
The results are presented from the different respondent groups’ points of view.
4.1 Audit companies
Current practices. Experience. Large audit companies (international networks) develop and
apply analytic tools that are similar to the BDA content-wise and complexity-wise. On
average, audit companies have applied modern analytic tools for two to four years in the
Baltic region. The auditors emphasise that the application of such innovative data analytics
in the Baltic region is actually not the first choice (as compared to the USA, the UK,
Germany or some Asian countries’ audit markets, for example). Big 4 auditors shared
similar practices:
We are a smaller country; therefore, we usually do not even get on the first wave of
implementation and application of innovative data analytics [Big 4 (2)].
However, some experts emphasised that companies had only taken the first steps in
analysing BD context, referring to the demand for BD-based tools:
We are making first steps but the practical implementation is not for today yet. [. . .] We are
developing applications, methodology. Some regions are more advanced, like North America, UK
or Asia. We [Lithuania] are more like recipients of innovations [Big 4 (1)].
Other experts confirmed that audit companies had already made a progress in developing
and applying analytical tools and had started to use the more advanced versions in
Lithuania:
[. . .] as we implement audit analytical tools very purposefully, now we develop and implement a
new and advanced analytical tool which was created and developed in UK office of our company
(International audit network).
Increasing trend. Conducting a BDA-based audit was a challenge for the auditors
themselves:
A possibility to audit all data is even now hardly perceivable for some auditors, as big companies’
audits are based on sampling methods. [. . .] With technologies, a huge amount of information in
an external audit does not play such an important role [Big 4 (1)].
Implementing BD technology-based tools establishes the conditions for changing the
thinking and attitudes of both auditors and business clients. In the case of a client being a
Table IV.
Axial codes derived
from second-order
codes
Second-order codes Description Axial codes
Current practices Arguments and descriptions related to the current
situation, experience and motivation to use BD/BDA
in companies
Current practices
Strategy-related company
factors
Different levels of the intensity of factors influencing
and motivating the level of BD/BDA use from the
internal environment of companies
Company factors
Resource-related company
factors
External factors Factors regulating, influencing and motivating the
level of BD/BDA use from the external environment
of companies
Institutional factors
Internal control The main areas that are influenced by the use of BD/
BDA in business and audit companies
Outcomes
Audit process
Quality
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small business company, audit companies even have to show the value of using BDA in the
audit process:
We indicate the main advantages of using BDA for our small or new clients [such as] using BDA
we will be able to indicate the systemic problems and variances in your [business] company data,
increase the quality of audit report and to find the fraud events (International audit network).
Benefits. The largest audit companies (international networks) assessed the BD and BDA
unambiguously positively and treated them as a competitive advantage in the audit market
in the long term. Enabling auditing technologies will probably foster the competitiveness of
all audit companies in the oligopoly audit market:
[. . .] currently, analytics tools are used considerably more, as also our company itself has invested
a lot into these new analytics tools. We think that Big 4 (2) Eagle [analytical tool] is a competitive
advantage. [. . .] Unambiguously positive, as it helps to focus on riskier fields. It helps to identify
the fields that might look suspicious [Big 4 (2)].
Financial resources. Small audit companies usually only apply very simple analytical tools,
mainly because of lack of knowledge, poor financial resources and the cost of investment.
The current practices of small- and medium-sized national audit companies and audit
companies that belong to international networks strongly diverge with regard to applying
modern technologies:
[. . .] by investing in analytical tools we always measure costs [. . .] as it’s really very expensive
[Big 4 (3)].
[. . .] notwithstanding huge financial recourses needed, all investments are very useful. We
operate in a very competitive business environment where we have to make our processes more
efficient in order to compete with a lower price. [. . .] Technologies help to work efficiently and
save costs (International audit network).
The largest companies were usually more experienced in the use of data analytics and were
already gaining advantages because of the economy of scale.
Institutional factors. Regulation system. Institutional factors affect audit companies
themselves through the requirements for the performance of more efficient audits
(application of control tests and detailed procedures) and quality control. Hence, the
importance of ISA is evident. Audit companies also have an impact via the client, such as
additional legislative requirements for the quality of accounting and clients’ accounting IS.
If audited clients are small, their accounting IS will naturally be distinguished by a
smaller quantity of structured and non-structured data. The size of the client is also
associated with the fee for the audit. In fact, no companies in the Baltic region are big
globally; therefore, strong competition in terms of price is prevalent.
[. . .] clients are too small, because if we talk about analytical tools, we encounter limitations, one
of which is the size of the client, and then this is closely associated also with price limitations [Big
4 (2)].
Although Krahel and Titera (2015) and Vasarhelyi et al. (2015) argued that the application of
BDA would also bring about changes in ISA, audit experts did not think that auditing
standards and methods should necessarily change for the successful employment of these
analytic tools. Current legal acts are sufficient to conduct a BD-based audit:
Audit standards that have these requirements already require all companies to conduct an audit
in the most effective way using the analytics tools. This is simply another tool to achieve these
goals in a faster and better way. But this does not change the way that an audit team should
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work, what the work principles are, how we plan, organise, review and what the quality control is
[Big 4 (2)].
Standards are nevertheless a set of principles, not rules. As regards an understanding of the
company, control environment and all processes, it is already laid down in the standards that you
have to understand all processes, irrespective of whether you will subsequently validate the
control or not, and whether you are going to trust them [Big 4 (4)].
Thus, auditing standards are focussed on the audit’s purpose and general principles, not on
the techniques/analytics that are used to perform it.
Market structure. It is important to note that the market orientation of client’s company
may also determine the use of BD technologies and the market’s size:
Lithuania is not a big market size. If companies are just orientated to the Lithuanian market, it is
not large enough. They do not require substantial systems that would work with crazy amounts
of data. [. . .] On the other hand, more and more service centres are being established in Lithuania
[banks, sharing centres (explanation added)]. . . . The driver would be management established in
a foreign country [Big 4 (1)].
Education. One of the most important aspects when attempting to apply BDA successfully
is having competent employees. Education plays a critical role in providing audit specialists
with interdisciplinary competence:
[. . .] even the universities themselves should focus more on IT by preparing specialists. It is a big
challenge for us. We can see IT specialists who do not care anything about accounting, and
graduated accountants who have poor skills in IT. Unfortunately, we do not see the merger. [. . .]
So we are already looking for people with integrated skills [Big 4 (1)].
By developing and implementing BDA we saw the transformation in the audit profession and it’s
not enough to be only an accountant or auditor but we also need to have IT competences. . .
(International audit network).
As requirements for external auditor’s professional competence are set by public authorities,
there may be inevitable changes in the long run.
Company factors. Strategy-related factors. The use of modern analytics in large network
audit companies, including international audit networks, is based on the global strategy of
IT innovations:
No large companies stand still, and, talking about our company, this is a really global
network investing in these technologies. [. . .] there exists a common global strategy and a
vision of the company, when we all [units in different regions] will start using a particular
analytics tool [Big 4 (2)].
To be a part of a global business and to belong to international networks, plays an
important role in using BD in external auditing and the client’s performance:
Most of the businesses, especially IT businesses, are foreign owned. They are driven by a parent
company. [. . .] So, the ownership structure is an important factor [Big 4 (1)].
The motivation of audit companies to invest in analytics tools relies primarily on the size of
the company and its strategic orientation. International audit networks and large audit
companies have greater possibilities of creating or acquiring such powerful analytics tools:
We do not develop such analytics tools in the Lithuanian unit. We use what has been globally
created in the company [Big 4 (2)].
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Notably, large audit companies (such as the Big 4) see BD as an increasingly essential part
of their assurance practice (Alles and Gray, 2016). It is important to note that the size of the
company determines the use of BD technologies not only due to the size of the audit
company itself but also based on the size of the audit client. The business client’s size was
one the most prevalent factors mentioned by the experts who were surveyed. If business
companies are small, their data are naturally not defined by the characteristics of 3Vs. This
theoretical presumption is consistent with the answers from regulators and auditors:
Multinational companies are big drivers. Facebook and Google are driving the auditors’
profession as well. We have to find ways to audit them and Big Data Analytics may help
[Big 4 (1)].
The size of a company can have an influence on the use of BD from the point of view of the
amount of data and probably in the future, even medium-sized companies will be able to apply
and use it (Global financial services and IT company).
Resource-related factors. Audit companies have to be prepared in terms of their internal
processes and capabilities to use BDA. They mainly need resources related to the
preparation of IS and integrated teams of employees for the successful application of BD and
BDA. As IT competencies are becoming extremely important, audit companies currently
resolve this issue by having an IT person in the company or outsourcing IT competence:
[. . .] We know what we want but we do not have IT competencies, so it’s better to take from
software companies. We are talking about major software companies like Microsoft, Oracle, SAP.
Obviously, the cooperation with these companies will help to develop the tools [Big 4 (1)].
We have an IT person who works with different groups and consult about IT questions [Big 4 (4)].
Outcomes. Audit process. For audit companies, BD may help to provide a better
understanding of the business client’s environment. All the experts interviewed claimed that
the application of these analytic tools made the audit process more effective, particularly
during the phase of understanding the client’s business environment and internal control
and during the phase of performing substantive procedures:
The reasons to perform an audit are more focused on risks, conduct it in a better, quality manner,
adapt to progress [Big 4 (2)].
Effectiveness is at the first place as competition by prices is essential. We are working totally in
electronic space [Big 4 (3)].
[. . .] our analytics show a certain tendency and variances in, for example, your [business client]
company and you [business client] are able to analyse detailed data where and why it [variances]
were found (International audit network).
An audit company, as a profit-seeking organisation, seeks to conduct an audit in the most
efficient way from the client’s and the quality point of view. Thus, analytic tools are one of
the instruments that reduce the screening risk, and thus, minimise the likelihood of incorrect
conclusions. Essential attention in the BD-based audit is paid to the verification of data
reliability. This is irrespective of whether the client’s information would be received in the
traditional way or via BDA; the issue of data reliability is always a priority:
The first work upon receipt of any information for auditing purposes is a test of its reliability.
[. . .] The main question during the verification of quality control is whether a data reliability test
has been made [Big 4 (4)].
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A set of BDA tools may also be beneficial for the drawing up of audit reports. During the
auditing process, co-operation is maintained with the company’s management, and different
reports may be drawn up (such as the auditor’s conclusion, the auditor’s report and letters to
the management). The final auditor’s conclusion is standardised, with clear criteria for the
information provided. Therefore, the BDA may have an indirect effect through the type of
auditor’s opinion. In other words, when applying more effective analytic tools, the
assumption is that the auditor had a better perception of the client’s environment, focussed
accordingly on the riskiest fields and decreased the likelihood of having provided an
incorrect opinion.
However, the possibility of using analytic technologies in other audit reports is much
greater and may create more added value for the client, only without the compulsory
compliance function:
A letter to the management where we share observations on internal control systems, their
shortcomings, provide recommendations that do not necessarily impede an audit, but we simply
share our insights. Thus, here we see very great possibilities that namely in this place [assessment
of the internal control system] the use of BDA would be of great help because [. . .] it would be an
analytics in different cross-sections [Big 4 (4)].
Quality. An audit market regulator and quality control may also be very important factors
fostering BDA in external audits. State regulation of the audit market is gradually growing
stronger across the world (SOX, Audit directives in the European Union, etc.). Thus, there is
noticeable pressure from individual audit quality regulators to apply more advanced
analytic tools in the audit process, which would translate into a better quality of risk-based
audits:
The need to apply advanced analytics tools arose not only from the audit teams themselves but
also from the quality control system. [. . .] An American regulator treats quality control systems
of audit companies extremely strictly and its audits are substantial. This is also the second strict-
wise and attitude-wise regulator in the Netherlands [Big 4 (4)].
Institutional quality control factors of external audit companies via the audit market
regulators in different markets produced a different effect:
Maybe, if we were only a national company and with this regulator, then we would probably have
less boost, but in fact, our global methodology team is in America and they work in the strongest
professional regulation environment. Thus, all approaches, all innovations, novelties and pressure
on the maintenance of audit quality come from over there [Big 4 (4)].
This is an approach of the global body that regulates all this audit policy [Big 4 (1)].
Internal control.When public interest companies are audited, the use of these tools becomes
an essential element for assessing the control system andmanaging the audit risk:
[. . .] one of our tools makes a very good report from the accountancy data, which makes it clear
whether a person has made any entries he cannot make and whether the duties are separated,
whether one and the same person does not do both, debit and credit, as this entails an additional
risk [Big 4 (2)].
Thus, there is a need for tools that would enable conducting an audit in an effective way, that
would enable to conduct it in a faster and better way, as quality may not be compromised either,
and the audit standards themselves, as I have mentioned, become not looser, but more stringent
[Big 4 (2)].
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Estimation of a client’s internal control system is one of the compulsory analytical
procedures for an auditor. The more complex and global the client company is, the more
multidimensional and complex is the internal control system of the client.
Issues related to the audit company. According to the research results, all second-order
codes were disclosed in the case of an audit company, and this could be explained as all
contingent factors influenced the use of BD and BDA, but the influence occurred at
different levels and degrees of importance. Our research results suggest that the use of
BD and BDA depends strongly on the audit corporate strategy and governance structure
and it confirms the research results of Verma and Bhattacharyya (2017). Moreover, it is
likely that BDA enables auditors to act on structured and unstructured information. In
line with Bhimani and Wilcocks (2014), we claim that the traditionally presumed
sequential and linear links among corporate strategy, governance structure and IS design
are no longer in play. This is the reason that we also suggest that, when applying the
BDA, additional attention should be paid to the company’s IS as one of the elements of the
internal control system. To a great extent, the IS depends on whether the auditor will be
inclined to trust the data or to apply more detailed audit procedures. The issue of the
reliability of the IS is crucial. Our study also suggests that the development of new
analytical competence and even a new structure of audit teams with regard to BDA is
necessary. In line with Al-Htaybat and Alhtaybat’s (2017) views on BD in corporate
reporting, building such teams (that include analytics) will require audit companies to
determine whether they want to outsource their analytics or whether they want to create
their own platforms and systems.
4.2 Business clients
Current practices. Experience and increasing trend. The use of BD and DA tools in business
companies (including international companies) is already the practice, with more than five
years of history and a trend towards expanded use in the future:
Banking sector was especially in a very good situation concerning BD because of regulation to
collect and save historical data. Analytics was just the next natural step forward (Financial
institution operating worldwide).
The implementation of BD technology-based tools establishes the conditions for changing
the thinking and attitudes of business companies:
BD is a global trend, everybody [business companies] can see and understand the value of using
BD and this understanding has become comprehensible to owners of businesses (Financial
institution operating worldwide).
Benefits. Business companies see BD and DA as an essential process in today’s business
environment and use them for a different purposes and benefits in areas such as cost saving,
planning processes, forecasting of the client’s behaviour and sales:
[. . .] there are a lot of areas where labour work could be changed with analytic [. . .] to predict the
client behaviour is one the possible usage of BD and another could be after-sale service (Financial
institution operating worldwide).
[. . .] each business unit has its own data analytics in different levels, such as risks, fraud, pricing,
transaction analytics, accounting analytics, marketing analytics (Global financial services and IT
company)
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Financial resources. Business companies see the implementation and use of BDA as a
process that is expensive and which requires a financial investment. The influence of this
concept is that it is mainly large companies that are able to integrate and use BDAwidely:
[. . .] from practical point of view, there are a small number of companies in Lithuania, which
could be able to use it [BDA]. It is understandable that you [Business Company] cannot expect
results from BD in six months, it is quite a long period and company has to understand this, you
have to invest and work (Financial institution operating worldwide).
Institutional factors. Regulation system. The sector regulator (such as the financial sector)
and the audit regulator play an important roles in the use of BDA:
[. . .] financial institutions historically must accumulate and save a different kind of data to
manage risk issues (Financial institution operating worldwide).
The audit regulator should encourage audit companies to be more advanced technologically, to
provide fresh news about novel audit analytics. Such topics are not even included in annual
training for auditors (National audit network company).
Market structure. The main motivating factors for using BD in business companies are
strong competition and long-term relationships with customers. Many interviewees
emphasised:
The main motivating factor is to create a sustainable relationship with customers (Financial
institution operating worldwide).
Competition is very strong in the market and a company needs to be better than its competitors,
so BD helps to ensure this aspect (Global financial services and IT company).
Education. These global trends influence the need for employees with broader interdisciplinary
competence, including knowledge about business, information technology and mathematics.
Business companies confirmed the importance and lack of competent employees globally:
[. . .] companies are lacking competent employees and looking for them, . . . it is very difficult to
find employees who would be ready to work in BDA area and even with experience (Financial
institution operating worldwide).
[.] there is an increasing level of interest from universities and study programmes but we still are
not able to find a fully prepared specialist able to work with BD. Mostly cases we invest in
competences improvement of those employees who have IT, mathematical or analytical skills
(Global financial services and IT company).
Company factors. Strategy-related factors. From the client’s perspective, the use of BDA and
DA rely heavily on the corporate strategy and top management’s support:
The main objective of all financial institutions operating worldwide group is BD integration into
business processes with purposes to minimise costs and to discover new possibilities for business
development (Financial institution operating worldwide).
[. . .] as changes are very fast in the market, decisions made have to be grounded by BD and
according to strategic choice of all company groups in all Europe and this is not limited to the
Lithuanian market (Global financial services and IT company).
Resource-related factors. Large companies will be more financially able to invest in new
technologies and capabilities (infrastructure and competent employees) and to invest in the
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future value that could be created by BD. In addition, it could be stated that companies in
developing countries might be able to integrate BDAmore quickly:
[. . .] because banking companies already started to develop business with more recent
information technologies and systems that allow to integrate BDA and to be more flexible
(Financial institution operating worldwide).
The main challenges for the application of BD in external auditing are the quality and
comparability of data and qualified BD analysts because companies need to have employees
who can find patterns in data and translate them into useful business information:
BD quality is very important . . . [. . .] We have two groups of BD, first is more raw data and using
it is allowed but risks need to be evaluated, second is fully prepared BD (Financial institution
operating worldwide).
The main internal challenge of using BD is HR and analytical skills integrating IT and business
skills. [. . .] Also, one more challenge is IT system and necessary investments into these systems,
consultancies (Financial institution operating worldwide).
Outcomes. Internal control. Business companies understand BD as the possible or the main
source of data to manage the business and use BDA tools for internal management, decision-
making, planning and reporting purposes:
We use BD in weekly control process by evaluating changes, influences and making decisions.
[. . .] Our expectations are that BD application will grow in the area of business process
development in the future. (Global financial services and IT company).
Issues related to business clients. The research results showed that not all second-order
codes were indicated in the case of business companies. In particular, the difference from
audit companies was in the area of outcomes. This could be explained by the fact that
business companies mainly use BD information for internal purposes to manage business
processes and make decisions. The research results confirmed that the possibility of
applying BD and BDA depended on the size of the business company and its strategic
orientation. Public interest companies, companies with international headquarters in
different countries, may encounter actual BD in their activities. The motivation to use
BDA and other DA is also important regardless of whether the client is a state-owned
company or a private company. The main motivation to use BD and BDA tools is related
to strong competition.
4.3 Regulator
Current practices. Increasing trend. Regulatory bodies understand the importance of BD/
BDA tools and see them as an increasing trend for all sectors, business companies, audit
companies and as a future direction in the case of regulatory bodies as these still do not have
experience in this area:
[. . .] our performance is very closely related with BD technologies. [. . .] because of looking at the
future all large business companies will need to provide all information to regulating
governmental institutions in electronic form starting from 2017 (Tax analytics).
Benefits. Regulatory bodies confirmed the usefulness of BD and BDA for large business
companies, governmental organisations and at the state level from the perspectives of time
and quality:
It [analytics tool] shows directions where mistakes, irregularities might be (Tax analytics).
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[. . .] this was the initiative from business companies. As The State Tax Inspectorate disrupted
companies with questions about different kind of data for two weeks, so it [BDA] is a benefit for
both parts (State Tax Inspectorate).
Financial resources. According to the experts interviewed, there is a need for e-audits and for
a funding project to support the implementation of e-audits, which will help to develop and
use BD-based analytic tools for different purposes:
There should be some actions taken and start a project implementation in a three-year period
(State Tax Inspectorate).
Cost benefit aspect is very important and we calculate the employees’ time saved for different
processes from regulator and business company sides, this helps to evaluate money saved in five
years, ten years or fifteen years (Tax analytics).
Institutional factors. Regulation system. Regulatory bodies play an important role at various
levels, such as in the tax environment, and in terms of sector regulation and audit regulation.
In the global regulation practice, it is still possible to notice different variants, ranging from
the compulsory universal certification of accounting systems to plans to certify accounting
information provided by companies:
Accounting systems are certified at the state level. [. . .] the same way an accountant must have a
certificate, an IS must be certified. [. . .] The future will unambiguously have to be this way, as the
number of errors due to low-quality information will make the process very painful (State Tax
Inspectorate).
According to the experts interviewed, one of the factors motivating the use of BDA will
definitely be the fostering of e-audits at the state level:
It is very important to make a breakthrough in the analytics, an audit breakthrough, a quality
leap so that we could audit banks not in the way we audited Snoras or U°kio bank. Positive audit
reports were issued and in a half-year, these banks became insolvent (explanation added) (State Tax
Inspectorate).
Education. Regulatory bodies indicated the future need to integrate educational institutions
in this increasing trend towards BD and BDA:
We plan to integrate researchers in the development of analytical tools. [. . .] there is still a lack of
knowledge and wisdom about the same understanding. Education would be able to play a key
role in this process (State Tax Inspectorate).
Outcomes. Audit process. Obviously, audit regulatory bodies do not participate directly in
the audit process, but their key function is the public oversight of quality control.
Responsible regulatory bodies evaluate how audit evidence is documented and the
compliance with ISA and the completeness of substantial audit procedures and control tests,
including audit evidence gathered via BD:
If transactions and accounting records are maintained in a ecentralizat way, a large company may
simply face the fact that data are wrong. Overall, the system seems to be correct, but
decentralization may show that, with time, these data have changed. This may be a big surprise
for such large companies [Regulator (2)].
Quality. As Lithuania abandoned national auditing standards in 2009, the Lithuanian audit
regulator does not have sufficient authority to change the implementation of the standards.
It is not the standard setter and has more of an advisory role:
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So, the biggest driver comes from international accounting settlers. [. . .] For the more advanced
regulators in Europe and other territories it is the tendency. As auditors, we move to a more
sophisticated IT environment of auditing the clients. The regulators have to understand how the
auditors audit. It might be even the beginning of the process [Big 4 (1)].
Internal control. Essentially, ISA lays down the provisions for assessing the client’s internal
control system, the IS and controls regarding the IS:
There are many different types of accounting software and auditors are familiar with some and
not familiar with others (Tax analytics).
The possibility of checking data in real time results in the likelihood that an audit may
create a higher value for the client. This would not only be an auditing process based on
historical data:
The reaction to on-going processes and the speed are very important. Now auditors make a
sampling and audit the data that is half-a-year, one-year old. [. . .] Thus, this reaction in current
time and controlling such data is very important to be able to react in a fast and expeditious
manner (Tax analytics).
Overall, auditors and regulators presented a conservative attitude towards incorporating
BD in decision-making for auditing aims. They admitted that BD played an important role,
but that the change will still be taking place in the future.
Regulator-related issues. The research results showed that second-order codes were
disclosed differently in the case of regulators. Company-related factors were not disclosed
because regulatory bodies are not treated in the same way as are companies. Regulatory
bodies still do not have current practice in the use of BD and BDA tools and the
implementation, thereof, is planned for the future. Institutional factors were disclosed
because regulatory bodies play an important role at various levels, such as in the tax
environment, in sector regulation and audit public oversight. Outcomes were mainly
disclosed with regard to quality, and this could be explained by the fact that regulatory
bodies are responsible for the public oversight of quality control, continuous learning and
education about innovative audit techniques, including BD and BDA. According to the
research results, regulatory bodies could be seen as followers of business and audit
companies in the use of BD and BDA tools.
5. Discussion and conclusion
5.1 Comparison and discussion of the results
Based on the qualitative research, we identified four key results. By disclosing a
comprehensive view of current practices (one), we identified two groups of motivating factors
[company-related (two) and institutional (three)] for the use of BDA from an external auditing
point of view, whichmay lead to the desired outcomes (four) for the audit companies.
Our findings showed that the current practice differed for business companies, audit
companies and regulators. Business companies had used BDA tools for more than five years
and saw this as an increasing trend in the future because of strong competition, and these
tools were used to understand the customers’ behaviour, to manage risk and for internal
management purposes. Hence, the use of BD and BDA was focussed mainly on the internal
management needs and market/sales expectations. Audit companies had approximately
three years of experience in the use of BDA tools. The use of modern analytics in large
network audit companies was usually based on the global strategy of IT innovations and
with the main purpose of ensuring the quality of the audit process and to issue a relevant
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auditor’s report. Regulatory bodies still did not have experience in the use of BD and BDA
tools and assume this would be an increasing trend in the future.
Our study, therefore, emphasises the importance of interdependence among audit
companies, business clients and regulators to enable the use of BD and BDA. Given this,
business companies might be the drivers of the use of BD and BDA tools and audit
companies might adopt these innovations because of high competition in the audit market.
Moreover, the current practices of business companies provided and even created suitable
conditions for external audit companies to use all the data (financial and non-financial,
structured and unstructured) for audit purposes. This motivates external audit companies to
use BDA as, firstly, business companies are able to provide BD and, secondly, the use of
BDA for audit purposes allows the achievement of the desired outcomes, such as the
efficiency and effectiveness of the audit, higher audit quality and minimising audit risk and
having a better understanding of the client’s business environment and internal control.
Specifically, the study has provided evidence of the importance of motivating factors and
circumstances that influence the use of BDA in external auditing process (Table V).
The results from the interviews showed that contingent factors may act both on the
company level (such as size, strategic orientation, structure and technology) and on the
institutional/external level (the audit market environment). What is more important is that
the influence of different contingent factors was not the same. Company-related factors had
a direct influence on the use of BDA in different phases of the audit, depending primarily on
the audit company’s data-driven strategy and the business client’s size. Moreover, the audit
market environment (the national regulator’s policy or the competition level) could be
assumed to be an indirect contingency factor because audit companies have to evaluate
environmental uncertainty and adapt to it.
Our findings showed that a company factor such as size influenced the use of BDA for both
audit companies and clients. These results are in contrast to the study by Li et al. (2018), who
found that corporate size did not influence the adoption of audit analytics in internal auditing
significantly. One reason could be that, if the audit client is extremely large, the client will be
confronted with plenty of semi-structured and unstructured massive data that cannot be
analysed using traditional audit software and analytics. On the other hand, only a large audit
company may have sufficient resources and substantial tools to be able to audit such a
company. This is also consistent with previous research stating that large companies have
extensive specialisation, standardisation and formalisation (Wickramasinghe and Alawattage,
2007), while small companies will not be able to provide all the necessary information as BD. In
addition, a small audit company would encounter challenges when attempting to use BDA
because of the lack of trained staff and technological capability (Alles, 2015).
With regard to the strategic orientation, our results are consistent with those of Li et al.
(2018) and Verma and Bhattacharyya’s (2017) findings that the major reason for the non-
adoption of BDA was that companies did not realise the strategic value of BDA, and they
were not ready to make changes due to technological, organisational and environmental
difficulties. Therefore, we conclude that a company’s strategic orientation and structure may
also be important influential factors concerning the use of BDA. On the other hand,
competent employees, internal capabilities and IS are resource-related audit company
factors because they are derived from the size of the company and from the strategic
orientation/attitude towards the adoption of technology. Moreover, audit companies attempt
to find a trade-off between the extent of information demanded by the environment and the
company’s available resources.
Audit market regulations and education may have a particular impact on an audit
company’s decision regarding the design of an audit strategy, such as how to apply modern
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auditing tools, how to ensure audit quality and what the topics for auditors’ training should
be. Our results are in line with Tarek et al. (2017) and Li et al. (2018), confirming that the
attitudes of audit regulatory bodies and legislative regulation followed by sector regulation
andmarket structure are critical for fostering the use of BDA.
Specifically, we provide the following theoretical and practical implications:
� Our paper expands on Li et al.’s (2018) study on understanding the use of audit
analytics for internal auditors due to several reasons. We aimed to investigate practices
pertaining to the use of BDA, in particular, (not all audit analytics in general) in external
auditing. Although external and internal auditors have similarities in terms of carrying
out audit procedures, the role of external auditors of decreasing information asymmetry
for capital markets is distinct and unique when compared to internal auditors.
Furthermore, external auditors must be independent and do not participate in an
Table V.
The highlights of
motivating factors
and circumstances
Motivating factors Motivating circumstances
Company-related
Size
Audit company’s size Audit companies with large international audit networks have more
capacity
Business client’s size Large business clients may have more BD
Strategic orientation
Data-driven strategy Data-driven strategy of the audit company
Client’s selected business
model
Usually business to consumer (B2C) experience more BD
Relationship between the
audit company and
business clients
In the case of a long-term contract, additional costs for initial
harmonisation and the correlation of different data sources
Structure
Audit company’s
structure
Global audit networks
Business client’s
ownership structure
In the case of a business company, public procurement has to be organised
for a state-owned company and, in most cases, only for one year
Sector Specific sectors in which BD is inherent, such as financial intermediation or
telecommunications
Technology
Digitalisation of the
business process
High degree of IT usage by audit companies and business clients
Accounting software used
by business clients
Technological level of accounting software. Usually BDA are not well
adapted for working with national accounting software, as there are
particular difficulties such as the extraction of data in the necessary
format, and initial processing to receive such data
Professionals with BDA
experience
Member of audit team/ outsourced professional/internal training
Institutional
Audit market
environment
Audit market competition High audit market competition. Strong price competition is prevalent in the
Baltic region
National audit regulator’s
policy
Help/support to acquire BDA or AA, provide training about analytics in
auditing
Education Higher education institutions to provide professionals with
interdisciplinary data analytic skills
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audited company’s activity constantly, as internal auditors do. This means that external
auditors have to gain understanding of the client’s environment and performance in a
very short time; hence, BDA might be a useful tool. While Li et al. (2018) emphasised
that only internal auditors should have more demand for the use of audit analytics to be
efficient and effective, the high prices and competition in the external audit market are
very important factors motivating the need to be more effective and implementing
more analytics. From the interviews, we may summarise that audit clients seek: to
negotiate for better pricing because of high competition in the audit market; and to get
more value and insights about corporate risks and performance. This leads to a trend
whereby external auditors are likely to focus on the procedures not just to satisfy
regulatory requirements, but to provide more value for the audit client; hence, BDA
may be one of the solutions.
� The results of our research also indicated diverse motivation in the use of BDA
depending on the business client’s size. Large business companies usually acted as
innovators in applying BD and audit companies were followers. In the case of the
client being a small business company, audit companies played a proactive role and
even had to demonstrate the value of using BDA in the audit process.
� The result that the national audit regulator was lagging behind in implementing
audit analytics was particularly problematic from a BD and BDA perspective. In
most cases, the national audit regulator played more of an advisory role, and was
currently lagging behind with regard to BD and BDA. From this perspective, the
study also outlined the dilemma of quality. Audit regulators need to ensure public
oversight of quality control and provide training for auditors. However, regulators
lacked knowledge about innovative BD-based techniques.
5.2 Conclusion and further research directions
The results of our research revealed audit companies’ intentions to use BDA and to expand
their understanding of the use of BD and BDA tools in external audits by emphasising the
close relationship of audit companies and different; yet, related groups such as business
clients and regulatory bodies.
We wish to emphasise the need to implement BD and BDA-based audit practices for
audit companies as a way to improve audit quality and to foster the efficiency of audits,
which may result in a competitive audit fee. This research also offers insights into helping to
customise their audit strategies.
In addition, our research results indicated that large business clients were the main drivers
of the use of BD and BDA in external auditing, as the current practices of large business
companies allow and create suitable conditions for audit companies to use BD (financial and
non-financial, structured and unstructured) for audit purposes. Large business clients usually
act as innovators in applying BD and BDA, while audit companies are followers. However, a
different direction in this relationship could be indicated in the case of small business clients, as
audit companies play a proactive role in this scenario and even have to show the additional
value of using BDA. Moreover, based on the interviews, we suggest that large networking
audit companies may gain long-term effectivity, which is important regardless of whether the
client is new or established. The other outcome is to ensure a higher audit quality resulting in
better value for the shareholders, the management and society.
For business clients and regulators, this study might help them to understand the
advantages and challenges of institutional and company factors concerning BDA use.
Big data and
big data
analytics
775
5.3 Contribution
Our study aims to contribute to the literature on auditing in the following ways. Firstly, it
adds to the small body of research by offering an empirical investigation the state-of-the-art
of BDA usage and motivating factors in external auditing. While prior studies (Li et al.,
2018) have focussed on internal auditing, this paper addresses BDA and external auditors in
particular. In addition, Verma and Bhattacharyya (2017) found that complexity and
perceived costs were the inhibitors that prevented the adoption of BDA in business
companies, while our research results indicated that the factors mentioned above were not
critical. Secondly, our study examines the phenomenon of BD and BDA in the context of
auditing. It is important to note that BD has specific characteristics compared to other types
of data and opportunities to use BD within BDA is of increasing importance for audit
companies, which to the authors’ knowledge, is absolutely new. Structured (around 10 per
cent) and unstructured (around 90 per cent) of data that are large in size cannot be analysed
using traditional software and database systems (Al-Htaybat and Alberti-Alhtaybat, 2017).
Thirdly, the paper presents a contingency-based theoretical framework as a model
explaining how different motivating factors may influence the use of BDA. The research
also makes a methodological contribution by using the approach of constructivist grounded
theory for the analysis of qualitative data.
5.4 Limitations
The conclusions of this study are based on interview data collected from 21 participants.
Future studies may investigate the issues addressed in this study further by using different
research sites and a broader range of data. Although the theoretical method is highly
transparent, it requires further testing to verify the mechanism on which it is based.
Furthermore, by keeping BDA as a tool, the use of which depends on the size of the company,
our sample yielded all interviews in particularly large companies. There is a limited number of
large companies in Lithuania that are open to co-operation. To test our research question more
broadly, we suggest including additional audit and business companies in future research.
5.5 Future research
There are a number of future research opportunities, as this is still a novel research area in the
field of auditing and accounting. Having chosen a qualitative approach prevents a broader data
collection method, which may provide different views. It would be worthwhile to carry out
further empirical analyses of BDA either currently or potentially in use through a detailed case
study or a quantitative survey to gather a broader range of insights. Our interview results
provided mixed results with regard to the need to change auditing standards and auditing
procedures when using BD. Thus, a deeper discussion of possible changes to audit procedures
could be another relevant area for future research. As we identified that the national audit
regulator is currently lagging behind in the area of audit analytics, it would be relevant to
investigate the quality dilemma from the perspective of public oversight of quality control and
the impact of international and national audit regulators on BDA and audit analytics in general.
Furthermore, it is worth conducting research on changes in external auditors’ profession through
education in analytical interdisciplinary skills. At the same time, future research could expand the
scope of BD and BDA research for the internal purposes of companies, such as internal auditing,
control processes and performance measurement. The interviewed experts confirmed the
importance of BD usage for the management of pricing, fraud detection, complaints and risk
assessment. Performance measurement integrated with BD would be able to support planning,
control and decision-making processes by providingmeaningful and appropriate information.
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Appendix
Table AI.
Interview guide
Questions to ensure
maintenance Enquiries
Why do you (not) use Big Data Analytics?
What is the motivation
behind this decision?
What is the corporate strategy regarding the use of modern data
analytics (Big 4)?
How long has the company been using Big Data Analytics and other
data analytic tools?
What are the benefits/costs of Big Data Analytics?
What internal factors drive your company to use Big Data Analytics?
What are internal factors
influencing the use of Big
Data Analytics?
What is the influence of the company’s size and the client’s size?
What is the influence on the auditing process in terms of:
Understanding the client and its environment,
Audit planning,
Sampling methods,
Other auditing techniques,
Auditing conclusion/reports?
What external factors drive your company to use Big Data Analytics?
Has external pressure
influenced the use of Big
Data Analytics?
What is the influence of the national regulative body?
What is the influence of the audit market’s size/competitors?
Which external groups – competitors, clients and other regulative
authorities have the biggest influence on the use of Big Data
Analytics?
How is (or how could) Big Data Analytics be implemented in the auditing process?
Who is involved in the
process of Big Data
Analytics?
Who prepares the Big Data? Who analyses the Big Data?
How do Big Data Analytics help to integrate non-traditional sources of
data with financial data?
How did your company create and implement Big Data Analytics?
Who created the Big Data
Analytics tools?
Do you use the services of IT consultancy companies?
Do you use your own capabilities?
Which changes do you expect in auditing?
Do you think Big Data
Analytics is a growing
trend?
Do you expect any
changes in the regulatory
framework?
What changes could there be concerning auditors’ competence?
Could there be a change from sample-based auditing to continuous
auditing?
What changes could there be for professional and educational
institutions?
Big data and
big data
analytics
781
About the authors
Dr. Lina Dagilien_e is a Professor at School of Economics and Business in Kaunas University of
Technology, Lithuania. Her research interests include sustainability accounting and reporting,
financial accounting and auditing issues. She is also interested in interdisciplinary projects due to
accounting sciences and is a developer of interdisciplinary graduate study programme “Business Big
Data”. Lina Dagilien_e is the corresponding author and can be contacted at: lina.dagiliene@ktu.lt
Dr. Lina Klovien_e is an Associate Professor at School of Economics and Business in Kaunas
University of Technology, Lithuania. She joined Kaunas University of Technology in 2012, before she
worked in a business company (Scandinavian capital bank in Lithuania) for nearly 8 years. Her main
research interests include the intersection of performance measurement/management control systems
and innovations.
For instructions on how to order reprints of this article, please visit our website:
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mailto:lina.dagiliene@ktu.lt
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reproduction prohibited without permission.
- Motivation to use big data and big data analytics in external auditing
1. Introduction
2. Literature review and theoretical framework
2.1 Literature review of big data analytics in external auditing
2.2 The theoretical framework
3. Research methodology
3.1 Data collection
3.2 The setting of the Lithuanian audit market
3.3 Coding and analyses
4. Results and findings
4.1 Audit companies
4.2 Business clients
4.3 Regulator
5. Discussion and conclusion
5.1 Comparison and discussion of the results
5.2 Conclusion and further research directions
5.3 Contribution
5.4 Limitations
5.5 Future research
References
sustainability
Article
Creating Sustainable Innovativeness through Big
Data and Big Data Analytics Capability: From the
Perspective of the Information Processing Theory
Michael Song, Haili Zhang * and Jinjin Heng
School of Economics and Management, Xi’an Technological University, Xi’an 720021, China;
michaelsong@xatu.edu.cn (M.S.); 1705210383@st.xatu.edu.cn (J.H.)
* Correspondence: zhanghaili@xatu.edu.cn
Received: 9 February 2020; Accepted: 2 March 2020; Published: 5 March 2020
����������
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Abstract: Service innovativeness is a key sustainable competitive advantage that increases
sustainability of enterprise development. Literature suggests that big data and big data analytics
capability (BDAC) enhance sustainable performance. Yet, no studies have examined how big
data and BDAC affect service innovativeness. To fill this research gap, based on the information
processing theory (IPT), we examine how fits and misfits between big data and BDAC affect service
innovativeness. To increase cross-national generalizability of the study results, we collected data from
1403 new service development (NSD) projects in the United States, China and Singapore. Dummy
regression method was used to test the model. The results indicate that for all three countries, high big
data and high BDAC has the greatest effect on sustainable innovativeness. In China, fits are always
better than misfits for creating sustainable innovativeness. In the U.S., high big data is always better
for increasing sustainable innovativeness than low big data is. In contrast, in Singapore, high BDAC
is always better for enhancing sustainable innovativeness than low BDAC is. This study extends
the IPT and enriches cross-national research of big data and BDAC. We conclude the article with
suggestions of research limitations and future research directions.
Keywords: big data; big data analytics capability; innovations and sustainability; information
processing theory; sustainable innovativeness
1. Introduction
The explosive growth of big data has brought opportunities and challenges for firms to rapidly
develop and improve their competitiveness and sustainability of the enterprise development [1,2].
Sustainable innovation, particularly service innovation, is a key driver of sustainable competitive
advantage [2]. Studies have demonstrated that big data is an invaluable resource in the development
of service innovation [2–4], but also places great demands on the information processing capability of
firms [5]. In the innovation literature, the information processing theory (IPT) [6] suggests that it is
important to consider the fit between information processing demands and information processing
capability [7,8]. IPT predicts that when there is a fit between a firm’s demands for information and its
information processing capability, the firm will gain greater sustainable competitive advantage. In the
era of big data, the big data processing and analysis requirements have increased significantly [4].
Firms need to use advanced technologies and tools, such as deep learning [5,9] and essential analytics
capability [10,11], to identify market trends and evolution patterns contained in big data. A lack of big
data analytics capability (BDAC) can leave firms with unharnessed big data, resulting in increased
data storage costs and greater difficulty in converting data into useful, timely information [12,13].
Big data refers to the enormous volume of rapidly and incessantly compiled data from an
immeasurable variety of market, consumer, social, and other activities. The increasingly digital modern
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Sustainability 2020, 12, 1984 2 of 23
era has seen the exponential growth of big data as an important information resource [14]. However,
extracting value from big data requires analysis and utilization capabilities that can translate big data
into usable information and create sustainable competitive advantages in innovation [12,15]. Thus,
BDAC has become the focus of many recent researches [2,5]. With BDAC, managers can gain new
perspectives and technologies to improve existing theoretical knowledge, enhance decision-making
capability, and promote innovation [5,10,16]. Many scholars have begun realizing the importance of fit
between big data and BDAC. Isik [4] pointed out that firms can align their big data processing demands
with their BDAC to effectively use big data to advance their products or competition mode. Wang and
Hajli [17] using the medical industry as their research setting, constructed a theoretical model of how
BDAC implements the integration, processing, and visualization of big data to achieve sustainable
growth in operational, organizational, management, and strategic areas. Hao et al. [2] examined the
positive moderating effect of BDAC on the relationship between big data and sustainable innovation
performance. Nevertheless, few researchers have focused on the measurement and empirical testing of
the fit between big data and BDAC [4] and there has been little in-depth discussion on the impact of
big data/BDAC fit on service innovation.
Innovativeness is a key indicator of service innovation success, which can help firms attract new
customers and obtain sustainable competitive advantages [18]. As service innovation is a process of
identifying and solving problems through the integration of resources and capabilities, the degree
of sustainable innovativeness is largely affected by the type and level of resources and capabilities
a firm has. The rapid development of big data has provided new development opportunities for
firms [11] by helping them quickly understand changing market demand, identify and create new
business opportunities, and achieve successful innovation [3,12,19,20]. BDAC encompasses a firm’s
ability to obtain a new strategic and operational perspective through the combination, integration,
and deployment of specific big data resources [10]. The effect of the fit between big data and BDAC
on sustainable innovativeness is thus very important in discussing the process of service innovation.
To facilitate our study of these issues, we developed three research questions:
RQ1: Do fits (the fit between high big data and high BDAC and the fit between low big data and
low BDAC) increase sustainable innovativeness more than misfits (the misfit between high big data
and low BDAC and the misfit between low big data and high BDAC) do?
RQ2: Does high-high fit (the fit between high big data and high BDAC) increase sustainable
innovativeness more than low-low fit (the fit between low big data and low BDAC) does?
RQ3: Does low-high misfit (the misfit between low big data and high BDAC) increase sustainable
innovativeness more than high-low misfit (the misfit between high big data and low BDAC) does? Or
is the reverse true?
To answer these three questions, we draw on the IPT to develop a theoretical model of the effects
of fits and misfits between big data and BDAC on sustainable innovativeness. We consider two types of
alignments (fits): the fit between high big data and high BDAC (high-high fit) and the fit between low
big data and low BDAC (low-low fit). We also evaluate two types of misfits: the misfit between high big
data and low BDAC (high-low misfit) and the misfit between low big data and high BDAC (low-high
misfit) (see Figure 1). Therefore, we examine four possible scenarios: high-low misfit, high-high fit,
low-low fit, and low-high misfit.
We empirically test the theoretical model and conduct a three-country comparative study to assess
its cross-national applicability by collecting data from 477 new service development (NSD) projects
in the United States, 632 NSD projects in China, and 294 NSD projects in Singapore. We use dummy
regression method to analyze the data.
Our study results suggest: (1) For the United States, China, and Singapore, high-high fit has the
greatest impact on sustainable innovativeness. (2) For China, sustainable innovativeness is higher
when big data and BDAC align (either high-high fit or low-low fit). Managers of NSD projects in China
should increase big data and BDAC simultaneously to ensure that they are always in balance. (3) For
the United States and Singapore, when either big data or BDAC is at a low level, fit is not always better
Sustainability 2020, 12, 1984 3 of 23
than misfit. The U.S. NSD projects should strive to improve the level of big data, while Singapore NSD
projects should focus on improving BDAC to achieve greater sustainable innovativeness.
3
Figure 1. Four scenarios of the fits and misfits between big
data and BDAC.
Our study results suggest: (1) For the United States, China, and Singapore, high-high fit has the
greatest impact on sustainable innovativeness. (2) For China, sustainable innovativeness is higher
when big data and BDAC align (either high-high fit or low-low fit). Managers of NSD projects in
China should increase big data and BDAC simultaneously to ensure that they are always in balance.
(3) For the United States and Singapore, when either big data or BDAC is at a low level, fit is not
always better than misfit. The U.S. NSD projects should strive to improve the level of big data, while
Singapore NSD projects should focus on improving BDAC to achieve greater sustainable
innovativeness.
We make three theoretical contributions to the literature on sustainability of big data application
and sustainable development theory: (1) We enrich research on the IPT by extending its application
to the context of big data and BDAC, defining information processing demands as big data and
information processing capability as BDAC. (2) We expand the empirical research on big data and
BDAC by exploring the impact of fits and misfits between big data and BDAC on sustainable
innovativeness. (3) We contribute to cross-national comparative research on sustainability of big data
and BDAC. Through empirical comparative analysis of data from the United States, China, and
Singapore, we find different impacts of fits and misfits between big data and BDAC on sustainable
innovativeness. The study results not only promote the application of the IPT to study of
sustainability of big data but also provide specific management suggestions for firms in different
countries to improve sustainable innovativeness through appropriate investment strategies for big
data and BDAC.
2.
2.1. Information Processing Theory (IPT)
The IPT regards a firm as an open social system that constantly exchanges information with the
external environment and utilizes that information in business activities [7,8]. Galbraith [8] described
the IPT as having three core concepts: information processing demand, information processing
capability, and the fit between this demand and capability. On the one hand, firms can reduce
information processing demand by increasing slack resources, but this strategy increases costs for
firms. On the other hand, firms can increase the availability of usable information to support decision-
making by improving information processing capability [7]. When the information processing
capability (collection, transformation, storage, and exchange of information) fit with the firm’s
Figure 1. Four scenarios of the fits and misfits between big data and BDAC.
We make three theoretical contributions to the literature on sustainability of big data application
and sustainable development theory: (1) We enrich research on the IPT by extending its application
to the context of big data and BDAC, defining information processing demands as big data and
information processing capability as BDAC. (2) We expand the empirical research on big data and
BDAC by exploring the impact of fits and misfits between big data and BDAC on sustainable
innovativeness. (3) We contribute to cross-national comparative research on sustainability of big
data and BDAC. Through empirical comparative analysis of data from the United States, China, and
Singapore, we find different impacts of fits and misfits between big data and BDAC on sustainable
innovativeness. The study results not only promote the application of the IPT to study of sustainability
of big data but also provide specific management suggestions for firms in different countries to improve
sustainable innovativeness through appropriate investment strategies for big data and BDAC.
2. Theoretical Background and Framework
2.1. Information Processing Theory (IPT)
The IPT regards a firm as an open social system that constantly exchanges information with the
external environment and utilizes that information in business activities [7,8]. Galbraith [8] described
the IPT as having three core concepts: information processing demand, information processing
capability, and the fit between this demand and capability. On the one hand, firms can reduce
information processing demand by increasing slack resources, but this strategy increases costs for firms.
On the other hand, firms can increase the availability of usable information to support decision- making
by improving information processing capability [7]. When the information processing capability
(collection, transformation, storage, and exchange of information) fit with the firm’s demand for
information processing, the firm can obtain sustainable competitive advantage. Since the IPT was first
proposed, many scholars have conducted research from the perspective of information processing to
explore the impact of fit between the demand for information and information processing capability on
firm performance. Most of the early research focused on strategy, structural design of the organization
or team, and supply chain management [21,22]. More recently, scholars have applied the IPT to
Sustainability 2020, 12, 1984 4 of 23
multiple research fields, including operations management, new product development, international
management, and knowledge management, which has further expanded the applicability of the
IPT [6,23,24]. However, most studies have applied the IPT to explore the fit between the traditional
needs for information and information processing capabilities [21,24], with few studies considering
the IPT in the context of big data and BDAC.
With the pervasiveness of big data in operations and organizational development, there is also
very high demand for specialized information processing capabilities. In the face of the rapidly
changing market environment, the value of big data is fleeting, and firms need timely and effective
analysis to mine the information resources in the big data [19]. There is no inevitable relationship
between the acquisition of information and the improvement of firm performance, only effective
use of the information can lead to improved profitability. The IPT considers the effective allocation
and coordination of a firm’s resources and capabilities such as how the adaptation and promotion of
different elements within a firm can effectively advance innovation activities [25]. BDAC provides
new information processing methods and technologies that enable firms to translate big data into new
information that can be used in different ways and promote sustainable service innovation. Although
some scholars have emphasized the importance of fit between big data processing demands and
big data processing capability based on the IPT [4], there is a lack of in-depth empirical testing and
consideration of the impact of fit in the field of service innovation. Therefore, in this study, we apply
the IPT by treating big data as the information processing demand of firms and BDAC as the important
information processing capability of firms, and discuss the impact of fit between big data and BDAC
on sustainable innovativeness in the process of service innovation.
2.2. Big Data
There is still no consensus on a definition of big data because of the wide range and rich meaning
it comprises [2]. Simply, big data refers to the large-scale data sets produced by new technology
forms. A deeper characterization of big data considers the sources and composition of these data
sets [1,3,10,14,19]. McAfee and Brynjolfsson [1] proposed that big data can be characterized according
to the 3V’s of volume, variety, and velocity. Other scholars have added two additional V’s of veracity
and value [14,26]. In this study, we define big data as large, complex, and real-time data streams that
require complex management, analysis, and processing techniques to extract valuable information [10].
However, the real value of big data lies not only in its large quantity but also, more importantly, in
its differences from traditional data. Big data has created a new and unique data generation and use
environment, which is not possible with a small amount of data [3,27].
Since the rise of the Internet and the digital economy, big data has become the most important
technological change in business and academia, bringing considerable benefits to business, scientific
research, public management, and other industries [1,2]. Many scholars have proposed that big data
is one of the most important resources for firms to achieve sustainable development [26,28]. For
example, big data can use production processes and supplier information to increase productivity,
reduce cost losses, and achieve sustainable corporate development [5]. Big data pervades modern life,
transforming thinking and decision-making methods and becoming an important strategic resource for
firms to achieve sustainable development [28]. Furthermore, as technology advances, the costs of big
data storage and BDAC technologies gradually decline, allowing more firms to realize the importance
of using and quantifying big data to enhance their competitive advantage [29].
Scholars have discussed the value of big data for firms from different perspectives. First, big
data is helpful for firms to understand market and demand information. It also provides new
perspectives for problem solving and enables firms to recombine existing resources and elements to
efficiently enhance firm innovation [30]. Big data also provides a database of timely information to
guide innovation activities, helping firms accurately predict market demand changes in a rapidly
changing environment, enabling quick response to market demand, and suggesting new development
directions and goals [3,19]. Second, the information provided by big data can enable managers to
Sustainability 2020, 12, 1984 5 of 23
make scientifically supported, high-quality decisions based on big data analytics rather than intuition
and experience [11,19]. The operational management perspective and new management knowledge
provided by big data can help managers make more efficient decisions [11]. Third, big data can help
managers better understand the information related to the market environment, customer demand,
and product characteristics and thereby improve the efficiency of operation processes [20,31]. The
basic information source provided by big data for managers can improve the efficiency of internal
information sharing and the operational outcome of firms [20]. In supply chain management, big
data can also help firms respond to the changing environment more quickly, reduce management
costs, and improve the efficiency of firm operation planning [31]. Finally, big data can help firms
identify opportunities and develop new business models to determine effective actions and strategies
for successful innovation [20,32].
2.3. Big Data Analytics Capability (BDAC)
With the growth of big data, firms have access to huge and diverse databases. Scholars introduced
the term data science to refer to the endeavor of effectively analyzing and visualizing the trends
and models contained in big data [5]. BDAC describes the tools and means employed to generate
information and knowledge from big data [14,26]. At present, most scholars define BDAC from
two perspectives: the resource-based view perspective and big data utilization process perspective.
From the perspective of the resource-based view, BDAC is an information technology capability that
provides perspective to firms by using data management, infrastructure, and human resources to gain
competitive advantage in the big data environment [14,33]. From the perspective of using big data
to create business value and scientific decision-making in business processes, BDAC describes the
ability of firms to analyze big data in planning, production, and transmission, thus enabling firms to
acquire, store, process, and analyze a large amount of data in various forms and extract valuable, timely
information [17,26]. In this study, we follow the research of [10] and define BDAC as the capability of
firms to combine, integrate, and deploy specific big data resources.
With the increasing importance of big data to firms, many scholars and managers have been
exploring how to make better use of BDAC to gain sustainable competitive advantage [26]. Research
on BDAC can be divided into the following four aspects: First, BDAC can significantly improve firm
performance [10,11,14,33]. In the context of big data, effective combination of organizational structure,
infrastructure, human capital, and other resources can help firms to obtain high-level competitive
advantage [14]. Second, BDAC can significantly affect the organizational agility of firms and improve
their capability to cope with environmental changes. BDAC can help managers accurately grasp
the rapidly changing market environment and propose corresponding business plans and solutions
to gain sustainable competitive advantage [14,15,34]. Third, BDAC promotes the improvement of
innovativeness of firms [16]. Rialti et al. [35] pointed out that BDAC can help firms to reintegrate
existing resources and routines to discover and take advantage of new opportunities and develop
innovative solutions to positively influence the innovation of firms. Fourth, BDAC can change business
processes and management modes, promote effective allocation and control of resources, and realize
business model innovation [17,30].
2.4. Sustainable Innovativeness
Innovativeness is an important measure of successful new product development, which is usually
described from the perspective of firms or customers [36]. As new service products are the main
achievements of NSD of firms, we draw from the results of previous research on product innovativeness
to define sustainable innovativeness as the degree of novelty of new service products compared with
existing service products and markets of firms [37,38].
NSD has become a key activity for firms to obtain sustainable development in a competitive
market environment. Sustainable innovativeness is the key factor of service innovation and one of
the important sources of sustainable competitive advantage. Therefore, the influencing factors of
Sustainability 2020, 12, 1984 6 of 23
sustainable innovativeness are of great interest to scholars and managers [39]. From the resource-based
view, relevant resources and information will significantly improve product innovativeness. The
market information owned by firms can help them effectively evaluate customer demand and market
trends and integrate them into the production of new service products, so as to develop new and
distinctive products [40]. Cillo et al. [41] pointed out that different analysis methods of market
information will have different effects on product innovativeness while Song et al. [38] found that
the marketing resources and research and development (R&D) resources of new ventures have
no significant impact on product innovativeness. Retrospective analysis of market information will
negatively affect product innovativeness, and prospective analysis of market information will positively
affect product innovativeness [41].
Previous research has considered the influencing factors of sustainable innovativeness from the
perspective of the firm’s capability to process resources and information, proposing that the firm’s
capability will affect sustainable innovativeness [18,39]. However, the relationship between a firm’s
knowledge integration mechanism and product innovativeness may not be a simple linear one; instead
some scholars have found that there is an inverted U-shaped relationship between them. Overemphasis
on knowledge synthesis, configuration, and applicable formal processes and structures among team
members can hinder the improvement of product innovativeness [42].
Many studies have found that information and resources are the key influencing factors of product
innovativeness. Extending these findings to the context of big data, the key to extracting value from
big data lies in the mining and analysis of big data by BDAC [10,19] and the key to the effective
implementation of BDAC lies in having sufficient big data resources [13]. Nevertheless, there has been
little in-depth examination of the fit between big data and BDAC, in particular with regard to the
impact mechanism of such fit on sustainable innovativeness. As a result, firms lack research-based
guidance on how to effectively maximize the value of their existing big data resources and BDAC in
service innovation. Therefore, pursuing research on the impact of fit between big data and BDAC on
sustainable innovativeness has important theoretical and practical significance.
3. Research Hypotheses
When there is fit between big data and BDAC, firms can fully mine their big data resources
for valuable information to build their knowledge base, improve the scientific basis and quality of
decision-making, and promote sustainable innovativeness. Based on the IPT, the fit between the
demand for information and information processing capability will result in more effective output [7].
Therefore, attaining fit between big data and BDAC can help NSD projects achieve successful innovation
activities more effectively and produce totally new service products that are novel and accepted by
customers, thus building sustainable development.
In the case of high-high fit, NSD project teams have access to a large amount of big data and
the high level of BDAC allows them to effectively analyze these data resources to obtain market and
customer demand information, clarify the development trend of service innovation [1,14,33], and
ultimately design novel service products [1].
In the case of low-low fit, the low level of big data leaves project teams unable to fully grasp the
changes in market demand [3] but also reduces the cost of information storage and the pressure of
information overload. At the same time, project teams can use the same level of BDAC to deeply mine
the data they have to acquire information that helps them identify service innovation market segments,
find the invention approaches to service innovation, and develop service products that can have an
important impact on the existing industry [16].
When there are misfits between big data and BDAC, project teams cannot effectively balance big
data resources and BDAC, which places project developers in the dilemma of a data storm that affects
their cognitive ability and decision-making quality [13]. Big data/BDAC misfit also increases the cost of
data storage, resulting in resource waste [7,12]. In the case of high-low misfit, although project teams
have a large amount of data, they lack BDAC and thus can merely interpret the data. In this situation,
Sustainability 2020, 12, 1984 7 of 23
the task of converting so much data into timely, usable information is difficult and overwhelming [14],
which can affect the accuracy of analysis of market trends and easily lead to blind development and,
ultimately, failure of service innovation [16].
In the case of low-high misfit, project managers have enough data mining technology to process,
analyze, and visualize big data [34], but they have access to few data resources and thus lower
requirements for BDAC. Such an imbalance will not only suppress sustainable innovativeness of
service products but also cause redundancy and waste of resources [7], hindering the innovation
activities of project teams. Thus, it is apparent that the roles of big data and BDAC are restricted by
each other. We therefore hypothesize:
Hypothesis 1 (H1). Fits (the fit between high big data and high BDAC and the fit between low big data and
low BDAC) improve sustainable innovativeness more than misfits (the misfit between high big data and low
BDAC and the misfit between low big data and high BDAC) do.
Although fit between big data and BDAC may be more beneficial than misfit, there are differences
in the impact on sustainable innovativeness between high-high fit and low-low fit. High levels of
both big data and BDAC enable project managers to use advanced analysis technologies to accurately
discover and classify important information from a massive variety of big data to identify new needs
of users or determine new market opportunities [33]. With such high-quality, timely information [10],
project managers can refine their goals for service innovation and achieve the leading position of
service product innovation in their industries.
In the case of low-low fit, because the project managers have a low stock of big data, they lack
timely and relevant information sources. Due to the low capability of data mining and analysis,
project teams are unable to fully grasp insights into market developments and service innovation and
thus suffer from a lack of service innovation inspiration and sustainable innovativeness [1,12]. We
therefore hypothesize:
Hypothesis 2 (H2). High-high fit (the fit between high big data and high BDAC) improves sustainable
innovativeness more than low-low fit (the fit between low big data and low BDAC) does.
When there are misfits between big data and BDAC, low-high misfit can improve sustainable
innovativeness more than high-low misfit can. In the case of low-high misfit, although project managers
do not have enough big data, the high level of BDAC can help them accurately find and sort out relevant
information from existing data, design service innovation process and operation measures, recombine
existing resources according to market demand, update product technology and functions [10,30],
and otherwise maximize the value of their limited big data resources. Even with a lower level of big
data, firms with advanced BDAC can carry out prospective analysis on existing market information,
predict market environment and development directions, clarify the direction of service innovation,
and effectively improve sustainable innovativeness [41].
In contrast, in the case of high-low misfit, although project managers have a large amount of
big data, they lack the capability to extract information on market demand trends and predictions
about consumption behavior, so they cannot effectively integrate and analyze the big data they have,
resulting in the lack of innovation spirit and the inability to accurately assess the direction of service
innovation [16]. Compared with low-high misfit, high-low misfit not only causes waste of resources
and increases the cost burden of project managers [12] but creates the dilemma of dealing with too
much information [16]. At the same time, big data itself will not be the source of differentiation
advantage for project teams [10] because compared with the big data resources owned by project
teams, BDAC is the key advantage to effectively utilizing market and customer information [14]. We
therefore hypothesize:
Sustainability 2020, 12, 1984 8 of 23
Hypothesis 3 (H3). Low-high misfit (the misfit between low big data and high BDAC) improves sustainable
innovativeness more than high-low misfit (the misfit between high big data and low BDAC) does.
4. Methodology and Data Sources
The data for the U.S. and China come from the research project conducted by Hao et al. [2]. The
details of the research methodology and data are described in Hao et al. [2]. For completeness, we
rephrase their descriptions here. The research design includes three empirical studies. We empirically
test the theoretical model of the impact of fit between big data and BDAC on sustainable innovativeness
using data from 477 U.S. NSD projects. We then test the generalizability of the model and compare
the similarities and differences between the United States and two other countries by conducting two
empirical studies to collect data from 632 NSD projects in China and 294 NSD projects in Singapore,
respectively [2]. We report these three empirical studies separately below.
As reported in Hao et al. [2], to develop and refine the study measures, the research team followed
the cross-national research methodology recommended by [43] to conduct in-depth interviews with
NSD teams in the United States, China, and Singapore. The final study measures and sources of the
measures are reported in the Appendix A.
4.1. Empirical Study 1: The United States
4.1.1. Measurement
Different from the measures used by Hao et al. [2], the measurement scale for big data in this
article includes five items that are adopted from Gupta and George [10]: (1) “We have access to very
large, unstructured, or fast-moving data for analysis”; (2) “We integrate data from multiple internal
sources into a data warehouse or mart for easy access”; (3) “We integrate external data with internal
data to facilitate high-value analysis of our business environment”; (4) “Our big data analytics projects
are adequately funded”; and (5) “Our big data analytics projects are given enough time to achieve
their objectives”. Project team leaders rated their agreement or disagreement with these descriptions
on a scale ranging from 0 (strongly disagree) to 10 (strongly agree). Based on factor analyses, item 5
was deleted.
The measurement items for BDAC are adopted from Hao et al. [2]. The specific measures
are reproduced in the Appendix A. A sample measure is “We have advanced tools (analytics and
algorithms) to extract values of the big data”. Project team leaders rated their team’s capabilities on a
scale ranging from 0 (no capability) to 10 (very high level of capability).
We adapted the five measurement items for sustainable innovativeness from Song and Parry [37].
As presented in Appendix A, minor modifications were made to the measures based on the in-depth
interviews and pretests. The final measures are: (1) “The products and services incorporate innovative
technologies that have never been used in the industry before”; (2) “The products and services caused
significant changes in the whole industry”; (3) “The products and services are among the first of their
kind to be introduced into the market”; (4) “The products and services are highly innovative—totally
new to the market”; (5) “The products and services are perceived as being the most innovative in the
industry”. Project team leaders rated their team’s sustainable innovativeness in these areas on a scale
ranging from 0 (strongly disagree) to 10 (strongly agree).
4.1.2. Data
As reported in Hao et al. [2], we chose 1000 U.S. firms from the Dun and Bradstreet database.
We used the same data collection procedure as reported in Hao et al. [2]. We sent, via express mail
and e-mail, a package/e-mail that included a personalized letter, the study survey, a pre-signed
non-disclosure agreement (NDA), and (for the mail package) a prepaid return envelope. We asked
each participating firm to select four different NSD projects for providing data: a “successful” NSD
Sustainability 2020, 12, 1984 9 of 23
project, a “failure” NSD project, a typical NSD project, and a recent NSD project. We sent a follow-up
letter/e-mail a week later. In addition, we sent second and third follow-up letters/e-mails and made
phone calls to nonresponding firms to improve the response rate.
For this study, we selected all 477 NSD projects collected using the above procedure. The final
data included 46 projects in hotel, traveling, and tourism services; 146 projects in banking, insurances,
securities, financial investments, and related activities; 99 projects in information and semiconductor;
95 projects in Internet-related services; and 91 projects in health care services [2].
4.1.3. Analysis and Results
Table 1 shows the mean, standard deviation, correlations, and construct reliability for the U.S.
sample. The values on the diagonal are Cronbach’s alpha coefficients for each variable, which are all
above the threshold value of 0.7, indicating that the study measures we employed have high reliability.
Table 1. The U.S. sample: descriptive statistics and correlation coefficient matrix (N = 477).
Innovativeness Big Data BDAC
Innovativeness 0.855
Big Data 0.587 *** 0.918
BDAC 0.433 *** 0.419 *** 0.803
Mean 5.717 5.315 6.044
S.D. 2.138 2.749 2.056
Note: *** p < 0.01 (two-tailed test). BDAC = Big data analytics capability. The Cronbach’s alpha for each variable is on the diagonal; the intercorrelations among the variables are on the off diagonal.
We also conducted exploratory factor analysis of the scale items. Table 2 shows the factor loadings
for the U.S. sample. For each measure to be included in the final analyses, it must load to the correct
factor with loading greater than 0.5 and must have no cross-loadings with loading greater than 0.4
in all three empirical studies. Item 5 of big data and item 3 of BDAC did not meet the requirements
and were deleted from the final analyses. The factor loadings of the remaining measures for the U.S.
sample are presented in Table 2. All final measures loaded correctly into the corresponding factor.
Table 2. The U.S. sample: factor loadings from exploratory factor analysis (N = 477).
Measure Items Innovativeness Big Data BDAC
INNO 1 0.833 0.187 0.190
INNO 4 0.772 0.229 0.086
INNO 2 0.723 0.260 0.125
INNO 3 0.722 0.178 0.238
INNO 5 0.671 0.272 0.181
Big Data 2 0.225 0.870 0.146
Big Data 4 0.268 0.868 0.149
Big Data 1 0.329 0.813 0.121
Big Data 3 0.262 0.784 0.313
BDAC 2 0.114 0.115 0.821
BDAC 1 0.135 0.162 0.759
BDAC 4 0.172 0.149 0.752
BDAC 5 0.204 0.137 0.720
Note: BDAC = Big data analytics capability. Bold numbers indicate items that load highly for the corresponding factor.
Before regression analysis, we used the sample mean value of big data (5.315) and the sample
mean value of BDAC (6.044) to divide the 477 NSD projects into four scenarios: two fits (high-high fit
and low-low fit) and two misfits (high-low misfit and low-high misfit), as shown in Figure 2.
Sustainability 2020, 12, 1984 10 of 23
10
Figure 2. The U.S. sample: fits and misfits between big data and BDAC (N = 477).
We used ordinary least squares (OLS) dummy regression to test the effect of two fits and two
misfits on sustainable innovativeness. Proc Reg of SAS 9.4 was used to provide estimates. As four
independent variables (two fits and two misfits) represent four dummy variables, option “noint” was
included in the model statement of the “Proc Reg” to exclude the intercept term in the “Proc Reg”
estimations. The estimated coefficients were the effects of fits and misfits on sustainable
innovativeness under four scenarios. To test the three hypotheses, we used the “TEST” statement of
the “Proc Reg Model” to examine whether or not the coefficients estimated in the model were
significantly different from each other as hypothesized. We tested for possible differences of all six
possible pairs and the results were all significant (p < 0.01).
Table 3 displays the final estimates. The results in Table 3 indicate that both fits and misfits have
significant positive impact on the sustainable innovativeness of NSD projects in the United States.
The results from six paired-wise tests indicate that these effects differ from each other (p < 0.01). To
examine whether or not each hypothesis is supported, we use the standardized estimates and the
results of the paired-wise tests.
As predicted by H1, the effect of high-high fit on sustainable innovativeness (b = 0.701; p < 0.01)
is the greatest. However, counter to H1, the positive effect of high-low misfit on sustainable
innovativeness (b = 0.400; p < 0.01) is greater than that of low-low fit (b = 0.384; p < 0.01). Thus, H1 is
only partially supported by the data.
The results suggest that the effect of high-high fit on sustainable innovativeness (b = 0.701; p <
0.01) is significantly higher than that of low-low fit (b = 0.384; p < 0.01). Thus, as predicted by H2,
high-high fit increases sustainable innovativeness more than low-low fit does (p < 0.01). The data
provide supports for H2.
H3 predicts that low-high misfit improves sustainable innovativeness more than high-low misfit
does. Counter to H3, the results in Table 3 indicate that the effect of low-high misfit on sustainable
innovativeness (b = 0.340; p < 0.01) is significantly lower, not higher (as hypothesized by H3), than
that of high-low misfit (b = 0.400; p < 0.01). Thus, H3 is not supported by the U.S. data.
Table 3. The U.S. sample: results of dummy regression analysis (N = 477).
Dependent Variable: Sustainable Innovativeness
Parameter Estimate (β) Standard Error (S.E.) Standardized Estimate (b)
Figure 2. The U.S. sample: fits and misfits between big data and BDAC (N = 477).
We used ordinary least squares (OLS) dummy regression to test the effect of two fits and two
misfits on sustainable innovativeness. Proc Reg of SAS 9.4 was used to provide estimates. As four
independent variables (two fits and two misfits) represent four dummy variables, option “noint” was
included in the model statement of the “Proc Reg” to exclude the intercept term in the “Proc Reg”
estimations. The estimated coefficients were the effects of fits and misfits on sustainable innovativeness
under four scenarios. To test the three hypotheses, we used the “TEST” statement of the “Proc Reg
Model” to examine whether or not the coefficients estimated in the model were significantly different
from each other as hypothesized. We tested for possible differences of all six possible pairs and the
results were all significant (p < 0.01).
Table 3 displays the final estimates. The results in Table 3 indicate that both fits and misfits have
significant positive impact on the sustainable innovativeness of NSD projects in the United States. The
results from six paired-wise tests indicate that these effects differ from each other (p < 0.01). To examine
whether or not each hypothesis is supported, we use the standardized estimates and the results of the
paired-wise tests.
Table 3. The U.S. sample: results of dummy regression analysis (N = 477).
Dependent Variable: Sustainable Innovativeness
Parameter Estimate (β) Standard Error (S.E.) Standardized Estimate
(b)
High-Low Misfit 6.118 *** 0.206 0.400
High-High Fit 6.963 *** 0.134 0.701
Low-Low Fit 4.179 *** 0.146 0.384
Low-High Misfit 5.380 *** 0.213 0.340
Model F-value 1263.050 ***
R-square 0.914
Adjusted R-square 0.914
Note: *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC. The six paired-wise tests indicate that all pairs are significantly different from each other at p < 0.01 (one-tailed test).
Sustainability 2020, 12, 1984 11 of 23
As predicted by H1, the effect of high-high fit on sustainable innovativeness (b = 0.701; p < 0.01) is the greatest. However, counter to H1, the positive effect of high-low misfit on sustainable innovativeness (b = 0.400; p < 0.01) is greater than that of low-low fit (b = 0.384; p < 0.01). Thus, H1 is only partially supported by the data.
The results suggest that the effect of high-high fit on sustainable innovativeness (b = 0.701; p < 0.01) is significantly higher than that of low-low fit (b = 0.384; p < 0.01). Thus, as predicted by H2, high-high fit increases sustainable innovativeness more than low-low fit does (p < 0.01). The data provide supports for H2.
H3 predicts that low-high misfit improves sustainable innovativeness more than high-low misfit
does. Counter to H3, the results in Table 3 indicate that the effect of low-high misfit on sustainable
innovativeness (b = 0.340; p < 0.01) is significantly lower, not higher (as hypothesized by H3), than that
of high-low misfit (b = 0.400; p < 0.01). Thus, H3 is not supported by the U.S. data.
4.2. Empirical Study 2: China
4.2.1. Measurement Validation in Empirical Study 2
As reported in Hao et al. [2], all measures were translated into Chinese using the double-translation
method [2] using four translators. Minor differences were discussed and resolved. Two pretests were
performed to evaluate the appropriateness of formats and accuracies using the participants of the
earlier interviewees. After pretests, minor modifications were made to formatting and wordings to
create the final survey [2].
4.2.2. Data
As reported in Hao et al. [2], to ensure comparability with the sample of the United States,
524 companies listed in the Small and Medium Enterprise and Growth Enterprise Market Boards of
the Shenzhen Stock Exchange in China were chosen as initial sampling frame. These companies were
further reduced to 482 companies to match with the sample from the United States after deleting all
companies with missing data. The details of the data collection were reported in [2]. This study used
all 632 NSD projects from the dataset. The final data included 40 from hotel, traveling, and tourism
services; 217 from banking, insurances, securities, financial investments, and related activities; 120 from
information and semiconductor; 91 from Internet-related services; and 164 from health care services [2].
4.2.3. Analysis and Results
Table 4 shows the descriptive statistics and correlation coefficient matrix of each variable for the
Chinese sample. The values on the diagonal are the Cronbach’s alpha coefficients of each variable,
all of which are greater than 0.7, indicating high reliability of our study measures. To ensure the
cross-national comparability of the data between China and the United States, we retained the same
measurement items for factor analysis as in the U.S. analysis. Table 5 shows the factor loadings of each
variable, which are all greater than 0.6, indicating high structural validity of the measurement items.
Table 4. The Chinese sample: descriptive statistics and correlation coefficient matrix (N = 632).
Innovativeness Big Data BDAC
Innovativeness 0.869
Big Data 0.588 *** 0.894
BDAC 0.389 *** 0.506 *** 0.767
Mean 5.297 4.571 6.254
S.D. 2.192 2.585 2.085
Note: *** p < 0.01 (two-tailed test). BDAC = Big data analytics capability. The Cronbach’s alpha for each scale is on the diagonal in italics; the intercorrelations among the variables are on the off diagonal.
Sustainability 2020, 12, 1984 12 of 23
Table 5. The Chinese sample: factor loadings from exploratory factor analysis (N = 632).
Measure Items Innovativeness Big Data BDAC
INNO 1 0.819 0.242 0.070
INNO 3 0.811 0.238 0.049
INNO 5 0.743 0.224 0.194
INNO 4 0.735 0.180 0.191
INNO 2 0.728 0.211 0.149
Big Data 1 0.243 0.865 0.159
Big Data 2 0.241 0.797 0.275
Big Data 3 0.252 0.767 0.210
Big Data 4 0.377 0.752 0.200
BDAC 1 0.035 0.237 0.800
BDAC 2 0.175 0.023 0.762
BDAC 5 0.066 0.241 0.730
BDAC 4 0.285 0.237 0.622
Note: BDAC = Big data analytics capability. Bold numbers indicate items that load highly for the corresponding factor.
Following analysis of the U.S. sample, we used the mean values of big data and BDAC to divide
the sample of Chinese NSD projects into four scenarios: two fits (high-high fit and low-low fit) and
two misfits (high-low misfit and low-high misfit), as shown in Figure 3.
12
Note: *** p < 0.01 (two-tailed test). BDAC = Big data analytics capability. The Cronbach’s alpha for each
scale is on the diagonal in italics; the intercorrelations among the variables are on the off diagonal.
Table 5. The Chinese sample: factor loadings from exploratory factor analysis (N = 632).
Measure Items Innovativeness Big Data BDAC
INNO 1 0.819 0.242 0.070
INNO 3 0.811 0.238 0.049
INNO 5 0.743 0.224 0.194
INNO 4 0.735 0.180 0.191
INNO 2 0.728 0.211 0.149
Big Data 1 0.243 0.865 0.159
Big Data 2 0.241 0.797 0.275
Big Data 3 0.252 0.767 0.210
Big Data 4 0.377 0.752 0.200
BDAC 1 0.035 0.237 0.800
BDAC 2 0.175 0.023 0.762
BDAC 5 0.066 0.241 0.730
BDAC 4 0.285 0.237 0.6
22
Note: BDAC = Big data analytics capability. Bold numbers indicate items that load highly for the
corresponding factor.
Following analysis of the U.S. sample, we used the mean values of big data and BDAC to divide
the sample of Chinese NSD projects into four scenarios: two fits (high-high fit and low-low fit) and
two misfits (high-low misfit and low-high misfit), as shown in Figure 3.
Figure 3. The Chinese sample: fits and misfits between big data and BDAC (N = 632).
We used OLS dummy regression analysis to test the impacts of the two fits and the two misfits
on sustainable innovativeness. Table 6 shows the results of dummy regression analysis. To test the
three hypotheses, we used the “TEST” statement of the “Proc Reg Model” to examine whether or not
the coefficients estimated in the model were significantly different from each other as hypothesized.
We tested for possible differences of all six possible pairs and the results were all significant (p<0.01).
Figure 3. The Chinese sample: fits and misfits between big data and BDAC (N = 632).
We used OLS dummy regression analysis to test the impacts of the two fits and the two misfits on
sustainable innovativeness. Table 6 shows the results of dummy regression analysis. To test the three
hypotheses, we used the “TEST” statement of the “Proc Reg Model” to examine whether or not the
coefficients estimated in the model were significantly different from each other as hypothesized. We
tested for possible differences of all six possible pairs and the results were all significant (p<0.01).
Sustainability 2020, 12, 1984 13 of 23
Table 6. The Chinese sample: results of dummy regression analysis (N = 632).
Dependent Variable: Sustainable Innovativeness
Parameter Estimate (β) Standard Error (S.E.) Standardized Estimate (b)
High-Low Misfit 5.660 *** 0.224 0.329
High-High Fit 6.748 *** 0.128 0.688
Low-Low Fit 4.130 *** 0.126 0.427
Low-High Misfit 4.653 *** 0.169 0.360
Model F-value 1315.420 ***
R-square 0.893
Adjusted R-square 0.893
Note: *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC. The six paired-wise tests indicate that all pairs are significantly different from each other at p < 0.01 (one-tailed test).
Our results show that both fits and misfits between big data and BDAC have significant positive
impacts on sustainable innovativeness in China. The results from six paired-wise tests indicate that
these effects differ from each other (p < 0.01). To examine whether or not each hypothesis is supported,
we use the standardized estimates and the results of the paired-wise tests.
Results in Table 6 indicate that the positive effects of high-high fit (b = 0.688; p < 0.01) and low-low fit (b = 0.427; p < 0.01) on sustainable innovativeness are greater than for high-low misfit (b = 0.329; p < 0.01) and low-high misfit (b = 0.360; p < 0.01). Therefore, when there is a fit between big data and BDAC, NSD projects can achieve higher sustainable innovativeness. Thus, H1 is supported by the Chinese data.
Consistent with H2, the effect of high-high fit (b = 0.688; p < 0.01) on sustainable innovativeness is higher than that of low-low fit (b = 0.427; p < 0.01), indicating that NSD projects with high levels of both big data and BDAC can achieve higher sustainable innovativeness. Thus, H2 is also supported by the data.
As predicted by H3, the positive effect of low-high misfit (b = 0.360; p < 0.01) on sustainable innovativeness is greater than that of high-low misfit (b = 0.329; p < 0.01). Therefore, H3 is also supported by the Chinese data.
4.3. Empirical Study 3: Singapore
4.3.1. Measurement Validation
To collect data in Singapore, we used the same measurement items as for the U.S. sample. As in
the Chinese sample, we distributed the study survey to 42 executives to conduct a pretest to ensure
that the expression of each item would be accurately understood by the participants in Singapore. We
made minor modifications on the formatting of the survey based on their feedback.
4.3.2. Data
To ensure comparability with the U.S. and China sample, companies were selected from the
Singapore Stock Exchange and supplemented with a list of members of four business associations in
Singapore. The data collection procedures described in the U.S. sample were adopted in Singapore.
We ultimately collected complete data for 294 NSD projects: 14 NSD in hotel, traveling, and tourism
services; 102 NSD in banking, insurances, securities, financial investments, and related activities; 62
NSD in information and semiconductor; 46 NSD in Internet-related services; and 70 NSD in health
care services.
Sustainability 2020, 12, 1984 14 of 23
4.3.3. Analysis and Results
The same data analyses are used to analyze the Singapore data. Table 7 shows the descriptive
statistics and correlation coefficient matrix of each variable for the Singapore sample. The values on the
diagonal are the Cronbach’s alpha coefficient for each variable, all of which are above 0.7, confirming
the high validity of our study measures. We also conducted factor analysis of the scale items. As shown
in Table 8, all factor loadings are between 0.641 and 0.884, indicating high structural validity of our
measurement scale.
Table 7. The Singaporean sample: descriptive statistics and correlation coefficient matrix (N = 294).
Innovativeness Big Data BDAC
Innovativeness 0.881
Big Data 0.566 *** 0.915
BDAC 0.393 *** 0.521 *** 0.775
Mean 4.298 3.430 6.353
S.D. 2.184 2.507 2.167
Note: *** p < 0.01 (two-tailed test). BDAC = Big data analytics capability. The Cronbach’s alpha for each scale is on the diagonal in italics; the intercorrelations among the variables are on the off diagonal.
Table 8. The Singaporean sample: factor loading of variables (N = 294).
Measure Items Innovativeness Big Data BDAC
Innovativeness INNO 1 0.854 0.249 0.117
INNO 3 0.850 0.123 0.002
INNO 2 0.778 0.116 0.222
INNO 4 0.700 0.335 0.105
INNO 5 0.679 0.397 0.199
Big Data Big Data 1 0.214 0.884 0.168
Big Data 2 0.273 0.842 0.205
Big Data 4 0.280 0.831 0.197
Big Data 3 0.243 0.744 0.225
BDAC BDAC 1 0.058 0.240 0.817
BDAC 2 0.096 0.002 0.743
BDAC 4 0.302 0.242 0.703
BDAC 5 0.062 0.413 0.641
Note: BDAC = Big data analytics capability. Bold numbers indicate items that load highly for the corresponding factor.
Following Study 1 and 2, we used the mean values of big data and BDAC to divide the Singapore
sample into fits (high-high fit and low-low fit) and misfits (high-low misfit and low-high misfit)
categories as shown in Figure 4.
We then used OLS dummy regression analysis to test the impacts of the fits and misfits between
big data and BDAC on sustainable innovativeness. To test the three hypotheses, we used the “TEST”
statement of the “Proc Reg Model” to examine whether or not the coefficients estimated in the model
were significantly different from each other as hypothesized. The results shown in Table 9 reveal
that the fits and misfits between big data and BDAC have significant positive impacts on sustainable
innovativeness. The results from six paired-wise tests indicate that these effects differ from each other
(p < 0.10).
Sustainability 2020, 12, 1984 15 of 23
15
Figure 4. The Singaporean sample: fits and misfits between big data and BDAC (N = 294).
Table 9. The Singaporean sample: results of dummy regression analysis (N = 294).
Dependent Variable: Sustainable Innovativeness
Parameter Estimate (β) Standard Error (S.E.) Standardized Estimate (b)
High-Low Misfit 5.144 *** 0.426 0.264
High-High Fit 6.091 *** 0.195 0.684
Low-Low Fit 3.215 *** 0.177 0.399
Low-High Misfit 3.642 *** 0.196 0.406
Model F-value 449.170 ***
R-square 0.861
Adjusted R-
square
0.859
Note: *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC;
High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big
data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC. The six
paired-wise tests indicate that all pairs are significantly different from each other at p < 0.10 (one-
tailed test).
To examine whether or not each hypothesis is supported, we used the standardized estimates
and the results of the paired-wise tests. The results in Table 9 indicate that high-high fit (b = 0.684; p
< 0.01) has the greatest impact on sustainable innovativeness. However, counter to H1, the positive
effect of low-low fit (b = 0.399; p < 0.01) on sustainable innovativeness is lower, not higher, than that
of low-high misfit (b = 0.406; p < 0.01). Thus, H1 is only partially supported by the Singapore
data.
We further find that the effect of high-high fit (b = 0.684; p < 0.01) on sustainable innovativeness
is greater than that of low-low fit (b = 0.399; p < 0.01), indicating that H2 is supported by the Singapore
data.
The date also shows that as predicted by H3, the effect of low-high misfit (b = 0.406; p < 0.01) on
sustainable innovativeness is greater than that of high-low misfit (b = 0.264; p < 0.01). Thus, H3 is
supported by the Singaporean data.
4.4. Summary of Hypothesis Testing for All Three Empirical Studies
Figure 4. The Singaporean sample: fits and misfits between big data and BDAC (N = 294).
Table 9. The Singaporean sample: results of dummy regression analysis (N = 294).
Dependent Variable: Sustainable Innovativeness
Parameter Estimate (β) Standard Error (S.E.) Standardized Estimate (b)
High-Low Misfit 5.144 *** 0.426 0.264
High-High Fit 6.091 *** 0.195 0.684
Low-Low Fit 3.215 *** 0.177 0.399
Low-High Misfit 3.642 *** 0.196 0.406
Model F-value 449.170 ***
R-square 0.861
Adjusted R-square 0.859
Note: *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC. The six paired-wise tests indicate that all pairs are significantly different from each other at p < 0.10 (one-tailed test).
To examine whether or not each hypothesis is supported, we used the standardized estimates
and the results of the paired-wise tests. The results in Table 9 indicate that high-high fit (b = 0.684;
p < 0.01) has the greatest impact on sustainable innovativeness. However, counter to H1, the positive
effect of low-low fit (b = 0.399; p < 0.01) on sustainable innovativeness is lower, not higher, than that of
low-high misfit (b = 0.406; p < 0.01). Thus, H1 is only partially supported by the Singapore data.
We further find that the effect of high-high fit (b = 0.684; p < 0.01) on sustainable innovativeness is greater than that of low-low fit (b = 0.399; p < 0.01), indicating that H2 is supported by the Singapore data.
The date also shows that as predicted by H3, the effect of low-high misfit (b = 0.406; p < 0.01) on sustainable innovativeness is greater than that of high-low misfit (b = 0.264; p < 0.01). Thus, H3 is supported by the Singaporean data.
4.4. Summary of Hypothesis Testing for All Three Empirical Studies
Table 10 summarizes the results of the six paired-wise tests for three empirical studies. The results
suggest the following results of the effects of fits and misfits on innovativeness:
Sustainability 2020, 12, 1984 16 of 23
1. In the United States, high-high fit > high-low misfit > low-low fit > low-high misfit (p < 0.01).
Therefore, H1 is partially supported because low-low fit < high-low misfit (not > as predicted by
H1); and H2 is supported. However, counter to H3, the effect of low-high misfit fit on sustainable
innovativeness is less, not higher (as predicted by H3), than High-Low Misfit is.
2. In China, high-high fit > low-low fit > low-high misfit > high-low misfit (p < 0.01). Therefore, all three hypotheses are supported as predicted.
3. In Singapore, high-high fit > low-high misfit > low-low fit > high-low misfit (p < 0.10). Therefore,
H1 is partially supported because low-low fit < low-high misfit (not > as predicted by H1); and
both H2 and H3 are supported.
Table 10. Summary results of three hypotheses in three countries.
Hypothesis Pair Comparison
The United
States
(N = 477)
China
(N = 632)
Singapore
(N = 294)
H1 (fits > misfits) High-High Fit > Low-High Misfit 39.680 *** 98.070 *** 78.300 ***
High-High Fit > High-Low Misfit 11.860 *** 17.760 *** 4.070 **
Low-Low Fit > Low-High Misfit 21.640 *** 6.180 *** 2.620 * (<)
Low-Low Fit > High-Low Misfit 59.020 *** (<) 35.350 *** 17.480 ***
H2 (HH > LL) High-High Fit > Low-Low Fit 197.290 *** 212.910 *** 119.450 ***
H3 (LH > HL) Low-High Misfit > High-Low Misfit 6.220 *** (<) 12.860 *** 10.240 ***
Note: Numbers in Table 10 are F-statistics. (<) indicates that the effect is “less, not higher as predicted by the hypothesis”. * p < 0.10; ** p < 0.05; *** p < 0.01 (because all hypotheses are directional, one-tailed test is used). High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC.
5. Cross-National Comparative Analyses
To explore the similarities and differences among our samples in the United States, China, and
Singapore, we summarize the standardized estimates of fits and misfits on sustainable innovativeness
in Table 11. The results suggest that a high level of big data matched with a high level of BDAC has
the greatest positive effect on sustainable innovativeness. The importance of the other three scenarios
differs across countries.
Table 11. Ranking of the standardized estimates of the effects of fits and misfits on
sustainable innovativeness.
Dependent Variable: Sustainable Innovativeness
Rank
The United States
(Standardized Estimate b)
China
(Standardized Estimate b)
Singapore
(Standardized Estimate b)
1 High-High Fit (0.701) High-High Fit (0.688) High-High Fit (0.684)
2 High-Low Misfit (0.400) Low-Low Fit (0.427) Low-High Misfit (0.406)
3 Low-Low Fit (0.384) Low-High Misfit (0.360) Low-Low Fit (0.399)
4 Low-High Misfit (0.340) High-Low Misfit (0.329) High-Low Misfit (0.264)
Note: High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big
data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit
between low big data and high BDAC.
In the United States, high-low misfit has a larger effect on sustainable innovativeness than
low-low fit and low-high misfit do. Low-high misfit has the least effect on sustainable innovativeness.
The significant differences are validated by the paired-wise tests (p < 0.01). Access to high big data
resources provides project leaders with rich information about markets, customers, and competitors
to inform innovation activities [19]. A low level of big data resources reduces project team’s ability
to accurately evaluate the market development and demand directions, resulting in misdirected
Sustainability 2020, 12, 1984 17 of 23
innovation activities and missed market opportunities. In addition, when big data is lacking, too
much BDAC can cause capacity redundancy and blur the focus of existing big data analysis, leading to
ineffective innovation activities.
In China, low-low fit has a larger impact on sustainable innovativeness than low-high misfit and
high-low misfit. Fits are better than misfits. Results of paired-wise tests in Table 10 suggest that the
differences are significant (p < 0.01). Thus, for NSD projects in China, it is important that the levels of
big data and BDAC be in alignment to support the improvement of sustainable innovativeness. When
there is high big data and low BDAC, projects are unable to meet the needs for data analysis, and
experience data overload and blind innovation.
In Singapore, a high level of BDAC can improve sustainable innovativeness: after high-high
fit, low-high misfit has the largest impact, followed by low-low fit and high-low misfit. Results of
paired-wise tests in Table 10 suggest that the differences are significant (p < 0.10). The effect of low-high
misfit on sustainable innovativeness is 1.538 times higher (0.406/0.264) than that of high-low misfit,
indicating that big data on its own is unlikely to be a source of competitive advantage for NSD projects
in Singapore [33], but a high level of BDAC can lead to effective mining and analysis of the available
big data to create benefits for NSD projects.
To further evaluate cross-national differences on how fits and misfits affect sustainable
innovativeness, we performed dummy regression analyses using pooled data of three countries.
The United States is the base case. Two country dummy variables (China and Singapore) and eight
interaction terms (country dummy variables multiply by four fits and misfits) were introduced into
the equation. Table 12 presents the results of the analyses. The four coefficient estimates for the four
interaction terms with China (or Singapore) as dummy variable show the differences between the
United States and China (or Singapore). The differences between China and Singapore can be evaluated
by using the sum of the coefficients (U.S. + China vs. U.S. + Singapore). We used “TEST” option in the
model statement of the “Proc Reg” to compare the estimates. We present the results in Table 13.
Table 12. Results of regression analysis using pooled data (N = 1403).
Dependent Variable: Sustainable Innovativeness
Independent Variables
Parameter Estimate
(β)
Standard Error
(S.E.)
Standardized Estimate
(b)
High-Low Misfit 6.118 *** 0.211 0.368
High-High Fit 6.963 *** 0.137 0.718
Low-Low Fit 4.179 *** 0.150 0.429
Low-High Misfit 5.380 *** 0.218 0.423
China × High-Low Misfit −0.458 0.304 −0.018
China × High-High Fit −0.215 0.185 −0.015
China × Low-Low Fit −0.049 0.194 −0.003
China × Low-High Misfit −0.727 *** 0.273 −0.038
Singapore × High-Low Misfit −0.974 ** 0.481 −0.019
Singapore × High-High Fit −0.873 *** 0.241 −0.038
Singapore × Low-Low Fit −0.963 *** 0.234 −0.046
Singapore × Low-High Misfit −1.738 *** 0.295 −0.075
Model F-value 1006.620 ***
R-square 0.897
Adjusted R-square 0.896
Note: ** p < 0.05; *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC. China = 1 if the sample is Chinese; 0 otherwise. Singapore = 1 if the sample is Singaporean; 0 otherwise. The base case is the United States.
Sustainability 2020, 12, 1984 18 of 23
Table 13. Testing results of the cross-national differences between China and Singapore.
China Singapore
Does the Effect Differ?
(F-Statistics and Significant Level)
The Effect of High-Low Misfit The Effect of High-Low Misfit 1.130
The Effect of High-High Fit The Effect of High-High Fit 7.900 ***
The Effect of Low-Low Fit The Effect of Low-Low Fit 17.700 ***
The Effect of Low-High Misfit The Effect of Low-High Misfit 15.300 ***
Note: *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC. Dummy variables: China = 1 if the sample is Chinese, 0 if not; Singapore = 1 if the sample is Singaporean, 0 if not.
The results in Tables 12 and 13 suggest that the coefficients for interaction terms (for both China
and Singapore) are all negative and that the numbers are more negative in Singapore than in China.
Therefore, the effects of fits and misfits on innovativeness is highest in the U.S. than in China and in
Singapore. The results suggest following additional cross-national differences for each of the scenarios:
(1) For effect of high-low misfit on sustainable innovativeness, the effect is less (β = −0.974; p < 0.05), in Singapore than in the U.S. There are no significant differences in the effect between U.S. and China (p > 0.10) and between China and Singapore (p > 0.10).
(2) For effect of high-high fit on sustainable innovativeness, the effect is the largest in the U.S.
(β = 6.963), the same in China (−0.215) but it is not significantly different from the U.S. with
p > 0.10), and the smallest in Singapore (β = 6.963–0.873= 6.090; p < 0.01). The results in Table 12
suggest that the difference between U.S. and Singapore is significant (p < 0.01). The results in
Table 13 indicate that the difference between China and Singapore is significant (p < 0.01).
(3) For effect of low-low fit on sustainable innovativeness, the effect is also the highest in the U.S.
(β = 4.179), the same in China (−0.049 but it is not significantly different from the U.S. with
p > 0.10), and the lowest in Singapore (β = 4.179–0.963= 3.216; p < 0.01). The results in Table 12
suggest that the difference between U.S. and Singapore is significant (p < 0.01). The results in
Table 13 indicate that the difference between China and Singapore is significant (p < 0.01).
(4) For low-high misfit on sustainable innovativeness, the effect is the highest in the U.S. (β = 5.380),
second in China (β = 5.380–0.727 = 4.653) and lowest in Singapore (β = 5.380–1.738 = 3.642).
The differences are all significant (p < 0.01).
6. Conclusions, Implications, and Future Research
6.1. Conclusions
Based on the IPT, we developed a theoretical model for studying the differential effects of fits and
misfits between big data and BDAC on sustainable innovativeness. We investigated four scenarios
and their impacts on sustainable innovativeness in a three-country comparative study. We tested
for significant differences between six pairs of the combinations and between the three pairs of the
countries. The empirical results provided at least partial supports for all three hypotheses.
First, as predicted by Hypothesis 1, we found that in China the effect of fits between big data
and BDAC on sustainable innovativeness is always stronger than that of misfits. However, in the
United States and Singapore, we found that the effect of low-low fit on sustainable innovativeness is
lower than that of misfits, indicating that the effect of fits between big data and BDAC on sustainable
innovativeness is not always stronger than that of misfits in these countries. This finding challenges
the assertions of previous research that fit between information, and information processing capability
is necessary to obtain value for the firm [4,7].
Second, as hypothesized in H3, across all three countries, we found that the positive impact of
high-high fit on sustainable innovativeness is greater than that of low-low fit. This finding supports the
conclusions of previous research that a high level of big data is a high-quality resource that can be fully
Sustainability 2020, 12, 1984 19 of 23
interpreted with a high level of BDAC to provide NSD project managers with insights into markets
and customers and thereby ensure the development of successful service products [10,19,30,33]. Our
finding that high levels of big data and BDAC can maximize sustainable innovativeness thus adds to
the results of Hao et al. [2], who suggested that when big data is high, improving BDAC will inhibit
innovation performance.
Third, we found significant differences in the impact of low-high misfit and high-low misfit
on sustainable innovativeness across the three countries. In the United States, the positive impact
of high-low misfit on sustainable innovativeness is higher than that of low-high misfit. This result,
consistent with Tan and Zhan [3], shows that rich big data resources can provide more sufficient,
reliable, and relevant information to guarantee the success of NSD projects even if BDAC is insufficient
to fully exploit these resources. Contrary to Song et al. [38], who found that the level of marketing
and R&D resources has an insignificant relationship with product innovativeness, we found that if
U.S. firms pursuing NSD projects lack big data resources, they cannot accurately obtain the valuable
information needed to ensure the sustainable innovativeness of service products. In contrast, in China
and Singapore, the impact of high-low misfit on sustainable innovativeness is less, not greater, than
that of low-high misfit. This result suggests that firms in China and in Singapore should operate
differently from firms in the U.S. They need to focus on increasing big data rather than BDAC to
successfully develop innovative service products. As Rialti et al. [35], Gupta and George [10], and
Ferraris et al. [11] have also found, even if there are limited big data resources, increasing BDAC
can enable project leaders to integrate and internalize existing big data information to improve the
sustainable innovativeness of projects.
Finally, the results from cross-national comparative analyses reveal four major conclusions. First,
the fits have greater effect on sustainable innovativeness in the U.S. and in China than that in Singapore.
Second, the impact of high-low misfit on sustainable innovativeness is higher in the U.S. than in
Singapore. Third, the positive effect of low-high misfit on sustainable innovativeness is the largest
in the U.S., followed by China, and then by Singapore. The possible reasons may be that there are
differences in the development speed of big data and analytics capability among the three countries.
Firms in the U.S. are better with applying big data and BDAC to develop innovative services and
products than firms in China and in Singapore are.
6.2. Theoretical Implications
This research enriches the literature on big data and innovation in several ways. First, this study
expands the application of the IPT with regard to big data. Previous studies on the IPT have focused on
firms’ need for traditional information sources and information processing capability [21,24]. However,
in the current marketplace, the need for information is largely affected by big data, which necessitates
higher information processing capability [19]. This study specifically considers big data and BDAC,
explores the application of the IPT in the context of big data and service innovation, and complements
existing research on the IPT [23,24].
Although other scholars such as Isik [4] have discussed the need for big data and information
processing capability and stressed the importance of their alignment to generate value from big data,
they have neither specified measurement items for these constructs nor conducted in-depth empirical
tests. Thus, this study fills these gaps in the empirical analysis of big data and BDAC by using fieldwork
and case studies to refine the definitions and connotations of big data and BDAC, improving existing
measurement scales, and proposing systematic measurement scales [14]. This study is also the first to
consider both fits and misfits between big data and BDAC and assess their impacts on sustainable
innovativeness. This not only enhances the previous research focusing only on the impact of big data or
BDAC [3,14,16,19] but also contributes to research on sustainable innovativeness [18] by demonstrating
the important impact of different configurations of fit between big data and BDAC in the context of
service innovation.
Sustainability 2020, 12, 1984 20 of 23
Finally, this study enriches the theory of cross-national big data management. Previous research
on big data and BDAC has mostly focused on the data of a single country [3,17,35]. In this study we
conducted a comparative analysis across three countries. By analyzing the data from NSD projects
in the United States, China, and Singapore, we explored the similarities and differences of fits and
misfits between big data and BDAC in the process of service innovation in these countries, building
the literature in this area.
6.3. Managerial Implications
The results of our analysis of the impact of fits and misfits between big data and BDAC on
sustainable innovativeness offer targeted recommendations for project managers in the different
countries to achieve successful service innovation.
First, when there are sufficient resources available, NSD project managers in the United States,
China, and Singapore should all invest in both big data and BDAC to improve sustainable innovativeness.
It is important that managers ensure the synchronous improvement of both big data and BDAC and
not emphasize the development of one aspect over the other.
Second, if resources are limited, then the recommended development strategies for project
managers differ among the three countries.
NSD project managers in the United States should invest in large amounts of high-quality big
data to ensure that the project always has a high level of big data resources to serve as the foundation
of the project. Project managers can improve their big data resources in four ways: (1) increase the
quantity and stock of big data as much as possible and constantly update the existing data to ensure its
timeliness so team members can understand changing market conditions and make timely adjustments
to the project; (2) build a data warehouse or mart to integrate various internal and external sources
of big data (e.g., customer demand, market development trends, business processing, competitor
information, etc.) and create a comprehensive knowledge base; (3) invest sufficient funds in NSD
projects so they can be fully developed; and (4) allocate time for effective analysis of big data to ensure
retention of reliable and relevant information, avoid decision-making mistakes, and achieve successful
project outcomes.
In China, managers can improve sustainable innovativeness by ensuring that big data and BDAC
maintain a balanced level. For example, if an NSD project has less big data, it should not invest in
further improving analysis tools and technologies but instead should focus on in-depth analysis of
existing data.
In Singapore, NSD project managers should focus on improvement of BDAC by investing in
pertinent analysis technologies and tools to enhance the ability of the project team to transform big data
into useful information. Managers can improve BDAC in three ways: (1) introduce advanced analysis
and algorithm tools, effectively analyze big data of different structure forms, extract all information
related to development activities, and find the connection between different processes and activities;
(2) focus on predicting potential market opportunities and development trends from existing data
resources; and (3) recruit high-quality team members with strong analytical skills and provide regular
training to assist team members in adapting to the development of technology and analysis tools.
Overall, project managers need to build a data-driven culture in their firm that supports big data
thinking and improves the sensitivity and cognitive ability of employees with regard to data.
6.4. Limitations and Future Research
There are several shortcomings of this study that can be improved upon in future work. We
focused here only on sustainable innovativeness as an important indicator of service innovation
output. Future studies should also consider how fits and misfits affect the quality of new service
products, the adoption of new service products, and innovation speed. These are all important
sustainable competitive advantages for sustainable service development. Furthermore, our study
sample included only five industries. Future studies should collect more data in other industries to
Sustainability 2020, 12, 1984 21 of 23
assess the generalizability of the research conclusions. Although we gained valuable insight from
our analysis of data from the United States, China, and Singapore, future endeavors can be enhanced
with data from other countries, particularly those that represent a variety of economic and cultural
systems, to further enrich cross-national comparative research and contribute to the understanding of
the sustainability of new service development.
Author Contributions: M.S. and H.Z. share the first-authorship of this article. H.Z. is corresponding author.
Conceptualization, M.S., J.H., and H.Z.; methodology, M.S. and H.Z.; data curation, H.Z. and M.S.; writing—original
draft preparation, J.H., H.Z., and M.S., writing-review and editing, M.S., J.H., and H.Z.; funding acquisition, H.Z.
All authors have read and agreed to the authorship and content of the article. All authors have read and agreed to
the published version of the manuscript.
Funding: This research was funded by the Humanities and Social Science Project of the China Ministry of
Education under the grant with project title: “Breakthrough service innovation: effects of big data analytics and
AI capability”. The partial funding was also supported by the Natural Science Foundation of Shaanxi Province of
China, grant number 2018JQ7003.
Acknowledgments: The authors thank assistant editor of Sustainability and two anonymous reviewers for their
useful suggestions which improve the quality of this article. The literature review and hypothesis development
were based on the graduation thesis of Jinjin Heng.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.
Appendix A. Study Measures and Sources
Big Data (adopted from Gupta and George [10]). (0 = strongly disagree; 5 = neutral; 10 =
strongly agree)
(1) We have access to very large, unstructured, or fast-moving data for analysis.
(2) We integrate data from multiple internal sources into a data warehouse or mart for easy access.
(3) We integrate external data with internal to facilitate high-value analysis of our
business environment.
(4) Our big data analytics projects are adequately funded.
(5) * Our big data analytics projects are given enough time to achieve their objectives.
Big Data Analytics Capability (BDAC) (adopted from Hao et al. [2]).
(1) We have advanced tools (analytics and algorithms) to extract values of the big data. (0 = no
capability; 5 = median level; 10 = very high level of capability; adopted from Hao et al. [2], which
was derived from Dubey et al. [34]; Gupta and George [10]).
(2) Our capability to discover relationships and dependencies from the big data is: (0 = no capability;
5 = neutral; 10 = very high level of capability; adopted from Hao et al. [2], which was developed
based on field research).
(3) * Our capability to perform predictions of outcomes and behaviors from the big data is: (0 = no
capability; 5 = median level; 10 = very high level of capability; adopted from Hao et al. [2], which
was derived from Gupta and George [10]).
(4) Our capability to discover new correlations from the big data to spot market demand trends and
predict user behavior is: (0 = no capability; 5 = median level; 10 = very high level of capability;
adopted from Hao et al. [2]; which was derived from Akter et al. [14]; Wamba et al. [33]).
(5) Our big data analytics staff has the right skills to accomplish their jobs successfully. (0 = none; 5 =
median level; 10 = very high level of capability; adopted from Hao et al. [2], which was derived
from Gupta and George [10]).
Sustainable Innovativeness (adapted from Song and Parry [37]). (Minor modifications were made based
on the pretests as reported in the text. The changes are shown below using the notations: deletion is
Sustainability 2020, 12, 1984 22 of 23
marked using
22
Sustainable Innovativeness (adapted from Song and Parry [37]). (Minor modifications were made
based on the pretests as reported in the text. The changes are shown below using the notations:
deletion is marked using deletion. Added text is marked with underline.) (0=strongly disagree;
5=neutral; 10=strongly agree).
(1) Our The products and services often incorporate innovative technologies which have never been
used in the industry before.
(2) Our The products and services caused significant changes in the whole industry.
(3) Our The products and services are one of the first of its kind introduced into the market.
(4) Our The products and services are highly innovative—totally new to the market.
(5) Our The products and services are perceived as most innovative in the industry.
Note: * indicates that the item was deleted based on factor analyses as described in the text.
.
1. Mcafee, A.; Brynjolfsson, E. Big data: The management revolution. Harv. Bus. Rev. 2012, 90, 60–68.
2. Hao, S.; Zhang, H.; Song, M. Big data, big data analytics capability, and sustainable innovation
performance. Sustainability 2019, 11, 7145.
3. Tan, K.H.; Zhan, Y. Improving new product development using big data: A case study of an electronics
company. R&D Manag. 2016, 47, 570–582.
4. Isik, Ö. Big Data Capabilities: An Organizational Information Processing Perspective. In Analytics and Data
Science. Annals of Information Systems; Deokar, A., Gupta, A., Iyer, L., Jones, M., Eds.; Springer: Berlin, 2018;
pp. 29–40.
5. George, G.; Osinga, E.C.; Lavie, D.; Scott, B. Big data and data science methods for management research.
Acad. Manag. J. 2016, 59, 1493–1507.
6. Peng, D.X.; Heim, G.R.; Mallick, D.N. Collaborative product development: The effect of project complexity
on the use of information technology tools and new product development practices. Prod. Oper. Manag.
2014, 23, 1421–1438.
7. Tushman, M.; Nadler, D. Information processing as an integrating concept in organization design. Acad.
Manag. Rev. 1978, 3, 613–624.
8. Galbraith, J.R. Organization design: An information processing view. Interfaces 1974, 4, 28–36.
9. LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444.
10. Gupta, M.; George, J.F. Toward the development of a big data analytics capability. Inf. Manag. Amst. 2016,
53, 1049–1064.
11. Ferraris, A.; Mazzoleni, A.; Devalle, A.; Couturier, J. Big data analytics capabilities and knowledge
management: Impact on firm performance. Manag. Deci. 2019, 57, 1923–1936.
12. Bumblauskas, D.; Nold, H.; Bumblauskas, P.; Igou, A. Big data analytics: Transforming data to action. Bus.
Proc. Manag. J. 2017, 23, 703–720.
13. Ghasemaghaei, M.; Ebrahimi, S.; Hassanein, K. Data analytics competency for improving firm decision
making performance. J. Strateg. Inf. Syst. 2018, 27, 101–113.
14. Akter, S.; Wamba, S.F.; Gunasekaran, A.; Dubey, R.; Childe, S.J. How to improve firm performance using
big data analytics capability and business strategy alignment. Int. J. Prod. Econ. 2016, 182, 113–131.
15. Jin, D.H.; Kim, H.J. Integrated understanding of big data, big data analysis, and business intelligence: A
case study of logistics. Sustainability 2018, 10, 3778.
16. Tan, K.H. Managerial perspectives of big data analytics capability towards product innovation. Strateg.
Direc. 2018, 34, 33–35.
17. Wang, Y.; Hajli, N. Exploring the path to big data analytics success in healthcare. J. Bus. Res. 2017, 70, 287–
299.
18. Su, Z.; Ahlstrom, D.; Li, J.; Cheng, D. Knowledge creation capability, absorptive capacity, and product
innovativeness. R&D Manag. 2013, 43, 473–485.
19. Johnson, J.S.; Friend, S.B.; Lee, H.S. Big data facilitation, utilization, and monetization: Exploring the 3Vs
in a new product development process. J. Prod. Innov. Manag. 2017, 34, 640–658.
20. Urbinati, A.; Bogers, M.; Chiesa, V.; Frattini, F. Creating and capturing value from big data: A multiple-
case study analysis of provider companies. Technovation 2019, 84–85, 21–36.
. Added text is marked with underline.) (0 = strongly disagree; 5 = neutral; 10
= strongly agree).
(1) The products and services ofter incorporate innovative technologies which have never been
used in the industry before.
(2) The products and services caused significant changes in the whole industry.
(3) The products and services are one of the first of its kind introduced into the market.
(4) The products and services are highly innovative—totally new to the market.
(5) The products and services are perceived as most innovative in the industry.
Note: * indicates that the item was deleted based on factor analyses as described in the text.
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http://dx.doi.org/10.1108/MD-01-2018-0119
http://dx.doi.org/10.1016/j.techfore.2019.119781
http://dx.doi.org/10.1111/1540-5885.1860357
http://dx.doi.org/10.1016/S0272-6963(99)00019-4
http://dx.doi.org/10.1016/j.jom.2010.07.003
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http://dx.doi.org/10.1111/j.1540-5885.2010.00766.x
http://dx.doi.org/10.1016/j.respol.2010.06.004
http://dx.doi.org/10.1016/j.indmarman.2015.02.030
http://dx.doi.org/10.1111/1540-5885.1350422
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- Introduction
- Research Hypotheses
- Methodology and Data Sources
- Cross-National Comparative Analyses
- Conclusions, Implications, and Future Research
- Study Measures and Sources
Theoretical Background and Framework
Information Processing Theory (IPT)
Big Data
Big Data Analytics Capability (BDAC)
Sustainable Innovativeness
Empirical Study 1: The United States
Measurement
Data
Analysis and Results
Empirical Study 2: China
Measurement Validation in Empirical Study 2
Data
Analysis and Results
Empirical Study 3: Singapore
Measurement Validation
Data
Analysis and Results
Summary of Hypothesis Testing for All Three Empirical Studies
Conclusions
Theoretical Implications
Managerial Implications
Limitations and Future Research
References
Week 6 Discussion Post Topic 2:
· All posts (both initial and responses) must be substantial (several paragraphs each) and each of your initial posts must be supported by 3 peer reviewed or authoritative sources, not including the textbook, cited properly in APA format.
PwC’s perspective on Big Data and analytics:
Review the video and using peer reviewed articles from the library, discuss if you agree or disagree with the presenters on the use of big data and data analytics. To further support your opinion, discuss how you feel data analytics should or could be used in managerial accounting or if you feel data analytics could or should not be used in managerial accounting. Be sure to provide specific examples including information from professional associations such as the IMA (Institute of Management Accountants) .
Peer reviewed articles from library:
Song, M., Zhang, H., & Heng, J. (2020). Creating sustainable innovativeness through big data and big data analytics capability: From the perspective of the information processing theory. Sustainability, 12(5), 1984. doi:http://dx.doi.org/10.3390/su12051984
Dagilienė, L., & Klovienė, L. (2019). Motivation to use big data and big data analytics in external auditing. Managerial Auditing Journal, 34(7), 750-782. doi:http://dx.doi.org/10.1108/MAJ-01-2018-1773
Creating_Sustainab
le_Innovativ
sustainability
Article
Creating Sustainable Innovativeness through Big
Data and Big Data Analytics Capability: From the
Perspective of the Information Processing Theory
Michael Song, Haili Zhang * and Jinjin Heng
School of Economics and Management, Xi’an Technological University, Xi’an 720021, China;
michaelsong@xatu.edu.cn (M.S.); 1705210383@st.xatu.edu.cn (J.H.)
* Correspondence: zhanghaili@xatu.edu.cn
Received: 9 February 2020; Accepted: 2 March 2020; Published: 5 March 2020
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Abstract: Service innovativeness is a key sustainable competitive advantage that increases
sustainability of enterprise development. Literature suggests that big data and big data analytics
capability (BDAC) enhance sustainable performance. Yet, no studies have examined how big
data and BDAC affect service innovativeness. To fill this research gap, based on the information
processing theory (IPT), we examine how fits and misfits between big data and BDAC affect service
innovativeness. To increase cross-national generalizability of the study results, we collected data from
1403 new service development (NSD) projects in the United States, China and Singapore. Dummy
regression method was used to test the model. The results indicate that for all three countries, high big
data and high BDAC has the greatest effect on sustainable innovativeness. In China, fits are always
better than misfits for creating sustainable innovativeness. In the U.S., high big data is always better
for increasing sustainable innovativeness than low big data is. In contrast, in Singapore, high BDAC
is always better for enhancing sustainable innovativeness than low BDAC is. This study extends
the IPT and enriches cross-national research of big data and BDAC. We conclude the article with
suggestions of research limitations and future research directions.
Keywords: big data; big data analytics capability; innovations and sustainability; information
processing theory; sustainable innovativeness
1. Introduction
The explosive growth of big data has brought opportunities and challenges for firms to rapidly
develop and improve their competitiveness and sustainability of the enterprise development [1,2].
Sustainable innovation, particularly service innovation, is a key driver of sustainable competitive
advantage [2]. Studies have demonstrated that big data is an invaluable resource in the development
of service innovation [2–4], but also places great demands on the information processing capability of
firms [5]. In the innovation literature, the information processing theory (IPT) [6] suggests that it is
important to consider the fit between information processing demands and information processing
capability [7,8]. IPT predicts that when there is a fit between a firm’s demands for information and its
information processing capability, the firm will gain greater sustainable competitive advantage. In the
era of big data, the big data processing and analysis requirements have increased significantly [4].
Firms need to use advanced technologies and tools, such as deep learning [5,9] and essential analytics
capability [10,11], to identify market trends and evolution patterns contained in big data. A lack of big
data analytics capability (BDAC) can leave firms with unharnessed big data, resulting in increased
data storage costs and greater difficulty in converting data into useful, timely information [12,13].
Big data refers to the enormous volume of rapidly and incessantly compiled data from an
immeasurable variety of market, consumer, social, and other activities. The increasingly digital modern
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Sustainability 2020, 12, 1984 2 of 23
era has seen the exponential growth of big data as an important information resource [14]. However,
extracting value from big data requires analysis and utilization capabilities that can translate big data
into usable information and create sustainable competitive advantages in innovation [12,15]. Thus,
BDAC has become the focus of many recent researches [2,5]. With BDAC, managers can gain new
perspectives and technologies to improve existing theoretical knowledge, enhance decision-making
capability, and promote innovation [5,10,16]. Many scholars have begun realizing the importance of fit
between big data and BDAC. Isik [4] pointed out that firms can align their big data processing demands
with their BDAC to effectively use big data to advance their products or competition mode. Wang and
Hajli [17] using the medical industry as their research setting, constructed a theoretical model of how
BDAC implements the integration, processing, and visualization of big data to achieve sustainable
growth in operational, organizational, management, and strategic areas. Hao et al. [2] examined the
positive moderating effect of BDAC on the relationship between big data and sustainable innovation
performance. Nevertheless, few researchers have focused on the measurement and empirical testing of
the fit between big data and BDAC [4] and there has been little in-depth discussion on the impact of
big data/BDAC fit on service innovation.
Innovativeness is a key indicator of service innovation success, which can help firms attract new
customers and obtain sustainable competitive advantages [18]. As service innovation is a process of
identifying and solving problems through the integration of resources and capabilities, the degree
of sustainable innovativeness is largely affected by the type and level of resources and capabilities
a firm has. The rapid development of big data has provided new development opportunities for
firms [11] by helping them quickly understand changing market demand, identify and create new
business opportunities, and achieve successful innovation [3,12,19,20]. BDAC encompasses a firm’s
ability to obtain a new strategic and operational perspective through the combination, integration,
and deployment of specific big data resources [10]. The effect of the fit between big data and BDAC
on sustainable innovativeness is thus very important in discussing the process of service innovation.
To facilitate our study of these issues, we developed three research questions:
RQ1: Do fits (the fit between high big data and high BDAC and the fit between low big data and
low BDAC) increase sustainable innovativeness more than misfits (the misfit between high big data
and low BDAC and the misfit between low big data and high BDAC) do?
RQ2: Does high-high fit (the fit between high big data and high BDAC) increase sustainable
innovativeness more than low-low fit (the fit between low big data and low BDAC) does?
RQ3: Does low-high misfit (the misfit between low big data and high BDAC) increase sustainable
innovativeness more than high-low misfit (the misfit between high big data and low BDAC) does? Or
is the reverse true?
To answer these three questions, we draw on the IPT to develop a theoretical model of the effects
of fits and misfits between big data and BDAC on sustainable innovativeness. We consider two types of
alignments (fits): the fit between high big data and high BDAC (high-high fit) and the fit between low
big data and low BDAC (low-low fit). We also evaluate two types of misfits: the misfit between high big
data and low BDAC (high-low misfit) and the misfit between low big data and high BDAC (low-high
misfit) (see Figure 1). Therefore, we examine four possible scenarios: high-low misfit, high-high fit,
low-low fit, and low-high misfit.
We empirically test the theoretical model and conduct a three-country comparative study to assess
its cross-national applicability by collecting data from 477 new service development (NSD) projects
in the United States, 632 NSD projects in China, and 294 NSD projects in Singapore. We use dummy
regression method to analyze the data.
Our study results suggest: (1) For the United States, China, and Singapore, high-high fit has the
greatest impact on sustainable innovativeness. (2) For China, sustainable innovativeness is higher
when big data and BDAC align (either high-high fit or low-low fit). Managers of NSD projects in China
should increase big data and BDAC simultaneously to ensure that they are always in balance. (3) For
the United States and Singapore, when either big data or BDAC is at a low level, fit is not always better
Sustainability 2020, 12, 1984 3 of 23
than misfit. The U.S. NSD projects should strive to improve the level of big data, while Singapore NSD
projects should focus on improving BDAC to achieve greater sustainable innovativeness.
3
Figure 1. Four scenarios of the fits and misfits between big data and BDAC.
Our study results suggest: (1) For the United States, China, and Singapore, high-high fit has the
greatest impact on sustainable innovativeness. (2) For China, sustainable innovativeness is higher
when big data and BDAC align (either high-high fit or low-low fit). Managers of NSD projects in
China should increase big data and BDAC simultaneously to ensure that they are always in balance.
(3) For the United States and Singapore, when either big data or BDAC is at a low level, fit is not
always better than misfit. The U.S. NSD projects should strive to improve the level of big data, while
Singapore NSD projects should focus on improving BDAC to achieve greater sustainable
innovativeness.
We make three theoretical contributions to the literature on sustainability of big data application
and sustainable development theory: (1) We enrich research on the IPT by extending its application
to the context of big data and BDAC, defining information processing demands as big data and
information processing capability as BDAC. (2) We expand the empirical research on big data and
BDAC by exploring the impact of fits and misfits between big data and BDAC on sustainable
innovativeness. (3) We contribute to cross-national comparative research on sustainability of big data
and BDAC. Through empirical comparative analysis of data from the United States, China, and
Singapore, we find different impacts of fits and misfits between big data and BDAC on sustainable
innovativeness. The study results not only promote the application of the IPT to study of
sustainability of big data but also provide specific management suggestions for firms in different
countries to improve sustainable innovativeness through appropriate investment strategies for big
data and BDAC.
2. Theoretical Background and Framework
2.1. Information Processing Theory (IPT)
The IPT regards a firm as an open social system that constantly exchanges information with the
external environment and utilizes that information in business activities [7,8]. Galbraith [8] described
the IPT as having three core concepts: information processing demand, information processing
capability, and the fit between this demand and capability. On the one hand, firms can reduce
information processing demand by increasing slack resources, but this strategy increases costs for
firms. On the other hand, firms can increase the availability of usable information to support decision-
making by improving information processing capability [7]. When the information processing
capability (collection, transformation, storage, and exchange of information) fit with the firm’s
Figure 1. Four scenarios of the fits and misfits between big data and BDAC.
We make three theoretical contributions to the literature on sustainability of big data application
and sustainable development theory: (1) We enrich research on the IPT by extending its application
to the context of big data and BDAC, defining information processing demands as big data and
information processing capability as BDAC. (2) We expand the empirical research on big data and
BDAC by exploring the impact of fits and misfits between big data and BDAC on sustainable
innovativeness. (3) We contribute to cross-national comparative research on sustainability of big
data and BDAC. Through empirical comparative analysis of data from the United States, China, and
Singapore, we find different impacts of fits and misfits between big data and BDAC on sustainable
innovativeness. The study results not only promote the application of the IPT to study of sustainability
of big data but also provide specific management suggestions for firms in different countries to improve
sustainable innovativeness through appropriate investment strategies for big data and BDAC.
2. Theoretical Background and Framework
2.1. Information Processing Theory (IPT)
The IPT regards a firm as an open social system that constantly exchanges information with the
external environment and utilizes that information in business activities [7,8]. Galbraith [8] described
the IPT as having three core concepts: information processing demand, information processing
capability, and the fit between this demand and capability. On the one hand, firms can reduce
information processing demand by increasing slack resources, but this strategy increases costs for firms.
On the other hand, firms can increase the availability of usable information to support decision- making
by improving information processing capability [7]. When the information processing capability
(collection, transformation, storage, and exchange of information) fit with the firm’s demand for
information processing, the firm can obtain sustainable competitive advantage. Since the IPT was first
proposed, many scholars have conducted research from the perspective of information processing to
explore the impact of fit between the demand for information and information processing capability on
firm performance. Most of the early research focused on strategy, structural design of the organization
or team, and supply chain management [21,22]. More recently, scholars have applied the IPT to
Sustainability 2020, 12, 1984 4 of 23
multiple research fields, including operations management, new product development, international
management, and knowledge management, which has further expanded the applicability of the
IPT [6,23,24]. However, most studies have applied the IPT to explore the fit between the traditional
needs for information and information processing capabilities [21,24], with few studies considering
the IPT in the context of big data and BDAC.
With the pervasiveness of big data in operations and organizational development, there is also
very high demand for specialized information processing capabilities. In the face of the rapidly
changing market environment, the value of big data is fleeting, and firms need timely and effective
analysis to mine the information resources in the big data [19]. There is no inevitable relationship
between the acquisition of information and the improvement of firm performance, only effective
use of the information can lead to improved profitability. The IPT considers the effective allocation
and coordination of a firm’s resources and capabilities such as how the adaptation and promotion of
different elements within a firm can effectively advance innovation activities [25]. BDAC provides
new information processing methods and technologies that enable firms to translate big data into new
information that can be used in different ways and promote sustainable service innovation. Although
some scholars have emphasized the importance of fit between big data processing demands and
big data processing capability based on the IPT [4], there is a lack of in-depth empirical testing and
consideration of the impact of fit in the field of service innovation. Therefore, in this study, we apply
the IPT by treating big data as the information processing demand of firms and BDAC as the important
information processing capability of firms, and discuss the impact of fit between big data and BDAC
on sustainable innovativeness in the process of service innovation.
2.2. Big Data
There is still no consensus on a definition of big data because of the wide range and rich meaning
it comprises [2]. Simply, big data refers to the large-scale data sets produced by new technology
forms. A deeper characterization of big data considers the sources and composition of these data
sets [1,3,10,14,19]. McAfee and Brynjolfsson [1] proposed that big data can be characterized according
to the 3V’s of volume, variety, and velocity. Other scholars have added two additional V’s of veracity
and value [14,26]. In this study, we define big data as large, complex, and real-time data streams that
require complex management, analysis, and processing techniques to extract valuable information [10].
However, the real value of big data lies not only in its large quantity but also, more importantly, in
its differences from traditional data. Big data has created a new and unique data generation and use
environment, which is not possible with a small amount of data [3,27].
Since the rise of the Internet and the digital economy, big data has become the most important
technological change in business and academia, bringing considerable benefits to business, scientific
research, public management, and other industries [1,2]. Many scholars have proposed that big data
is one of the most important resources for firms to achieve sustainable development [26,28]. For
example, big data can use production processes and supplier information to increase productivity,
reduce cost losses, and achieve sustainable corporate development [5]. Big data pervades modern life,
transforming thinking and decision-making methods and becoming an important strategic resource for
firms to achieve sustainable development [28]. Furthermore, as technology advances, the costs of big
data storage and BDAC technologies gradually decline, allowing more firms to realize the importance
of using and quantifying big data to enhance their competitive advantage [29].
Scholars have discussed the value of big data for firms from different perspectives. First, big
data is helpful for firms to understand market and demand information. It also provides new
perspectives for problem solving and enables firms to recombine existing resources and elements to
efficiently enhance firm innovation [30]. Big data also provides a database of timely information to
guide innovation activities, helping firms accurately predict market demand changes in a rapidly
changing environment, enabling quick response to market demand, and suggesting new development
directions and goals [3,19]. Second, the information provided by big data can enable managers to
Sustainability 2020, 12, 1984 5 of 23
make scientifically supported, high-quality decisions based on big data analytics rather than intuition
and experience [11,19]. The operational management perspective and new management knowledge
provided by big data can help managers make more efficient decisions [11]. Third, big data can help
managers better understand the information related to the market environment, customer demand,
and product characteristics and thereby improve the efficiency of operation processes [20,31]. The
basic information source provided by big data for managers can improve the efficiency of internal
information sharing and the operational outcome of firms [20]. In supply chain management, big
data can also help firms respond to the changing environment more quickly, reduce management
costs, and improve the efficiency of firm operation planning [31]. Finally, big data can help firms
identify opportunities and develop new business models to determine effective actions and strategies
for successful innovation [20,32].
2.3. Big Data Analytics Capability (BDAC)
With the growth of big data, firms have access to huge and diverse databases. Scholars introduced
the term data science to refer to the endeavor of effectively analyzing and visualizing the trends
and models contained in big data [5]. BDAC describes the tools and means employed to generate
information and knowledge from big data [14,26]. At present, most scholars define BDAC from
two perspectives: the resource-based view perspective and big data utilization process perspective.
From the perspective of the resource-based view, BDAC is an information technology capability that
provides perspective to firms by using data management, infrastructure, and human resources to gain
competitive advantage in the big data environment [14,33]. From the perspective of using big data
to create business value and scientific decision-making in business processes, BDAC describes the
ability of firms to analyze big data in planning, production, and transmission, thus enabling firms to
acquire, store, process, and analyze a large amount of data in various forms and extract valuable, timely
information [17,26]. In this study, we follow the research of [10] and define BDAC as the capability of
firms to combine, integrate, and deploy specific big data resources.
With the increasing importance of big data to firms, many scholars and managers have been
exploring how to make better use of BDAC to gain sustainable competitive advantage [26]. Research
on BDAC can be divided into the following four aspects: First, BDAC can significantly improve firm
performance [10,11,14,33]. In the context of big data, effective combination of organizational structure,
infrastructure, human capital, and other resources can help firms to obtain high-level competitive
advantage [14]. Second, BDAC can significantly affect the organizational agility of firms and improve
their capability to cope with environmental changes. BDAC can help managers accurately grasp
the rapidly changing market environment and propose corresponding business plans and solutions
to gain sustainable competitive advantage [14,15,34]. Third, BDAC promotes the improvement of
innovativeness of firms [16]. Rialti et al. [35] pointed out that BDAC can help firms to reintegrate
existing resources and routines to discover and take advantage of new opportunities and develop
innovative solutions to positively influence the innovation of firms. Fourth, BDAC can change business
processes and management modes, promote effective allocation and control of resources, and realize
business model innovation [17,30].
2.4. Sustainable Innovativeness
Innovativeness is an important measure of successful new product development, which is usually
described from the perspective of firms or customers [36]. As new service products are the main
achievements of NSD of firms, we draw from the results of previous research on product innovativeness
to define sustainable innovativeness as the degree of novelty of new service products compared with
existing service products and markets of firms [37,38].
NSD has become a key activity for firms to obtain sustainable development in a competitive
market environment. Sustainable innovativeness is the key factor of service innovation and one of
the important sources of sustainable competitive advantage. Therefore, the influencing factors of
Sustainability 2020, 12, 1984 6 of 23
sustainable innovativeness are of great interest to scholars and managers [39]. From the resource-based
view, relevant resources and information will significantly improve product innovativeness. The
market information owned by firms can help them effectively evaluate customer demand and market
trends and integrate them into the production of new service products, so as to develop new and
distinctive products [40]. Cillo et al. [41] pointed out that different analysis methods of market
information will have different effects on product innovativeness while Song et al. [38] found that
the marketing resources and research and development (R&D) resources of new ventures have
no significant impact on product innovativeness. Retrospective analysis of market information will
negatively affect product innovativeness, and prospective analysis of market information will positively
affect product innovativeness [41].
Previous research has considered the influencing factors of sustainable innovativeness from the
perspective of the firm’s capability to process resources and information, proposing that the firm’s
capability will affect sustainable innovativeness [18,39]. However, the relationship between a firm’s
knowledge integration mechanism and product innovativeness may not be a simple linear one; instead
some scholars have found that there is an inverted U-shaped relationship between them. Overemphasis
on knowledge synthesis, configuration, and applicable formal processes and structures among team
members can hinder the improvement of product innovativeness [42].
Many studies have found that information and resources are the key influencing factors of product
innovativeness. Extending these findings to the context of big data, the key to extracting value from
big data lies in the mining and analysis of big data by BDAC [10,19] and the key to the effective
implementation of BDAC lies in having sufficient big data resources [13]. Nevertheless, there has been
little in-depth examination of the fit between big data and BDAC, in particular with regard to the
impact mechanism of such fit on sustainable innovativeness. As a result, firms lack research-based
guidance on how to effectively maximize the value of their existing big data resources and BDAC in
service innovation. Therefore, pursuing research on the impact of fit between big data and BDAC on
sustainable innovativeness has important theoretical and practical significance.
3. Research Hypotheses
When there is fit between big data and BDAC, firms can fully mine their big data resources
for valuable information to build their knowledge base, improve the scientific basis and quality of
decision-making, and promote sustainable innovativeness. Based on the IPT, the fit between the
demand for information and information processing capability will result in more effective output [7].
Therefore, attaining fit between big data and BDAC can help NSD projects achieve successful innovation
activities more effectively and produce totally new service products that are novel and accepted by
customers, thus building sustainable development.
In the case of high-high fit, NSD project teams have access to a large amount of big data and
the high level of BDAC allows them to effectively analyze these data resources to obtain market and
customer demand information, clarify the development trend of service innovation [1,14,33], and
ultimately design novel service products [1].
In the case of low-low fit, the low level of big data leaves project teams unable to fully grasp the
changes in market demand [3] but also reduces the cost of information storage and the pressure of
information overload. At the same time, project teams can use the same level of BDAC to deeply mine
the data they have to acquire information that helps them identify service innovation market segments,
find the invention approaches to service innovation, and develop service products that can have an
important impact on the existing industry [16].
When there are misfits between big data and BDAC, project teams cannot effectively balance big
data resources and BDAC, which places project developers in the dilemma of a data storm that affects
their cognitive ability and decision-making quality [13]. Big data/BDAC misfit also increases the cost of
data storage, resulting in resource waste [7,12]. In the case of high-low misfit, although project teams
have a large amount of data, they lack BDAC and thus can merely interpret the data. In this situation,
Sustainability 2020, 12, 1984 7 of 23
the task of converting so much data into timely, usable information is difficult and overwhelming [14],
which can affect the accuracy of analysis of market trends and easily lead to blind development and,
ultimately, failure of service innovation [16].
In the case of low-high misfit, project managers have enough data mining technology to process,
analyze, and visualize big data [34], but they have access to few data resources and thus lower
requirements for BDAC. Such an imbalance will not only suppress sustainable innovativeness of
service products but also cause redundancy and waste of resources [7], hindering the innovation
activities of project teams. Thus, it is apparent that the roles of big data and BDAC are restricted by
each other. We therefore hypothesize:
Hypothesis 1 (H1). Fits (the fit between high big data and high BDAC and the fit between low big data and
low BDAC) improve sustainable innovativeness more than misfits (the misfit between high big data and low
BDAC and the misfit between low big data and high BDAC) do.
Although fit between big data and BDAC may be more beneficial than misfit, there are differences
in the impact on sustainable innovativeness between high-high fit and low-low fit. High levels of
both big data and BDAC enable project managers to use advanced analysis technologies to accurately
discover and classify important information from a massive variety of big data to identify new needs
of users or determine new market opportunities [33]. With such high-quality, timely information [10],
project managers can refine their goals for service innovation and achieve the leading position of
service product innovation in their industries.
In the case of low-low fit, because the project managers have a low stock of big data, they lack
timely and relevant information sources. Due to the low capability of data mining and analysis,
project teams are unable to fully grasp insights into market developments and service innovation and
thus suffer from a lack of service innovation inspiration and sustainable innovativeness [1,12]. We
therefore hypothesize:
Hypothesis 2 (H2). High-high fit (the fit between high big data and high BDAC) improves sustainable
innovativeness more than low-low fit (the fit between low big data and low BDAC) does.
When there are misfits between big data and BDAC, low-high misfit can improve sustainable
innovativeness more than high-low misfit can. In the case of low-high misfit, although project managers
do not have enough big data, the high level of BDAC can help them accurately find and sort out relevant
information from existing data, design service innovation process and operation measures, recombine
existing resources according to market demand, update product technology and functions [10,30],
and otherwise maximize the value of their limited big data resources. Even with a lower level of big
data, firms with advanced BDAC can carry out prospective analysis on existing market information,
predict market environment and development directions, clarify the direction of service innovation,
and effectively improve sustainable innovativeness [41].
In contrast, in the case of high-low misfit, although project managers have a large amount of
big data, they lack the capability to extract information on market demand trends and predictions
about consumption behavior, so they cannot effectively integrate and analyze the big data they have,
resulting in the lack of innovation spirit and the inability to accurately assess the direction of service
innovation [16]. Compared with low-high misfit, high-low misfit not only causes waste of resources
and increases the cost burden of project managers [12] but creates the dilemma of dealing with too
much information [16]. At the same time, big data itself will not be the source of differentiation
advantage for project teams [10] because compared with the big data resources owned by project
teams, BDAC is the key advantage to effectively utilizing market and customer information [14]. We
therefore hypothesize:
Sustainability 2020, 12, 1984 8 of 23
Hypothesis 3 (H3). Low-high misfit (the misfit between low big data and high BDAC) improves sustainable
innovativeness more than high-low misfit (the misfit between high big data and low BDAC) does.
4. Methodology and Data Sources
The data for the U.S. and China come from the research project conducted by Hao et al. [2]. The
details of the research methodology and data are described in Hao et al. [2]. For completeness, we
rephrase their descriptions here. The research design includes three empirical studies. We empirically
test the theoretical model of the impact of fit between big data and BDAC on sustainable innovativeness
using data from 477 U.S. NSD projects. We then test the generalizability of the model and compare
the similarities and differences between the United States and two other countries by conducting two
empirical studies to collect data from 632 NSD projects in China and 294 NSD projects in Singapore,
respectively [2]. We report these three empirical studies separately below.
As reported in Hao et al. [2], to develop and refine the study measures, the research team followed
the cross-national research methodology recommended by [43] to conduct in-depth interviews with
NSD teams in the United States, China, and Singapore. The final study measures and sources of the
measures are reported in the Appendix A.
4.1. Empirical Study 1: The United States
4.1.1. Measurement
Different from the measures used by Hao et al. [2], the measurement scale for big data in this
article includes five items that are adopted from Gupta and George [10]: (1) “We have access to very
large, unstructured, or fast-moving data for analysis”; (2) “We integrate data from multiple internal
sources into a data warehouse or mart for easy access”; (3) “We integrate external data with internal
data to facilitate high-value analysis of our business environment”; (4) “Our big data analytics projects
are adequately funded”; and (5) “Our big data analytics projects are given enough time to achieve
their objectives”. Project team leaders rated their agreement or disagreement with these descriptions
on a scale ranging from 0 (strongly disagree) to 10 (strongly agree). Based on factor analyses, item 5
was deleted.
The measurement items for BDAC are adopted from Hao et al. [2]. The specific measures
are reproduced in the Appendix A. A sample measure is “We have advanced tools (analytics and
algorithms) to extract values of the big data”. Project team leaders rated their team’s capabilities on a
scale ranging from 0 (no capability) to 10 (very high level of capability).
We adapted the five measurement items for sustainable innovativeness from Song and Parry [37].
As presented in Appendix A, minor modifications were made to the measures based on the in-depth
interviews and pretests. The final measures are: (1) “The products and services incorporate innovative
technologies that have never been used in the industry before”; (2) “The products and services caused
significant changes in the whole industry”; (3) “The products and services are among the first of their
kind to be introduced into the market”; (4) “The products and services are highly innovative—totally
new to the market”; (5) “The products and services are perceived as being the most innovative in the
industry”. Project team leaders rated their team’s sustainable innovativeness in these areas on a scale
ranging from 0 (strongly disagree) to 10 (strongly agree).
4.1.2. Data
As reported in Hao et al. [2], we chose 1000 U.S. firms from the Dun and Bradstreet database.
We used the same data collection procedure as reported in Hao et al. [2]. We sent, via express mail
and e-mail, a package/e-mail that included a personalized letter, the study survey, a pre-signed
non-disclosure agreement (NDA), and (for the mail package) a prepaid return envelope. We asked
each participating firm to select four different NSD projects for providing data: a “successful” NSD
Sustainability 2020, 12, 1984 9 of 23
project, a “failure” NSD project, a typical NSD project, and a recent NSD project. We sent a follow-up
letter/e-mail a week later. In addition, we sent second and third follow-up letters/e-mails and made
phone calls to nonresponding firms to improve the response rate.
For this study, we selected all 477 NSD projects collected using the above procedure. The final
data included 46 projects in hotel, traveling, and tourism services; 146 projects in banking, insurances,
securities, financial investments, and related activities; 99 projects in information and semiconductor;
95 projects in Internet-related services; and 91 projects in health care services [2].
4.1.3. Analysis and Results
Table 1 shows the mean, standard deviation, correlations, and construct reliability for the U.S.
sample. The values on the diagonal are Cronbach’s alpha coefficients for each variable, which are all
above the threshold value of 0.7, indicating that the study measures we employed have high reliability.
Table 1. The U.S. sample: descriptive statistics and correlation coefficient matrix (N = 477).
Innovativeness Big Data BDAC
Innovativeness 0.855
Big Data 0.587 *** 0.918
BDAC 0.433 *** 0.419 *** 0.803
Mean 5.717 5.315 6.044
S.D. 2.138 2.749 2.056
Note: *** p < 0.01 (two-tailed test). BDAC = Big data analytics capability. The Cronbach’s alpha for each variable is
on the diagonal; the intercorrelations among the variables are on the off diagonal.
We also conducted exploratory factor analysis of the scale items. Table 2 shows the factor loadings
for the U.S. sample. For each measure to be included in the final analyses, it must load to the correct
factor with loading greater than 0.5 and must have no cross-loadings with loading greater than 0.4
in all three empirical studies. Item 5 of big data and item 3 of BDAC did not meet the requirements
and were deleted from the final analyses. The factor loadings of the remaining measures for the U.S.
sample are presented in Table 2. All final measures loaded correctly into the corresponding factor.
Table 2. The U.S. sample: factor loadings from exploratory factor analysis (N = 477).
Measure Items Innovativeness Big Data BDAC
INNO 1 0.833 0.187 0.190
INNO 4 0.772 0.229 0.086
INNO 2 0.723 0.260 0.125
INNO 3 0.722 0.178 0.238
INNO 5 0.671 0.272 0.181
Big Data 2 0.225 0.870 0.146
Big Data 4 0.268 0.868 0.149
Big Data 1 0.329 0.813 0.121
Big Data 3 0.262 0.784 0.313
BDAC 2 0.114 0.115 0.821
BDAC 1 0.135 0.162 0.759
BDAC 4 0.172 0.149 0.752
BDAC 5 0.204 0.137 0.720
Note: BDAC = Big data analytics capability. Bold numbers indicate items that load highly for the corresponding factor.
Before regression analysis, we used the sample mean value of big data (5.315) and the sample
mean value of BDAC (6.044) to divide the 477 NSD projects into four scenarios: two fits (high-high fit
and low-low fit) and two misfits (high-low misfit and low-high misfit), as shown in Figure 2.
Sustainability 2020, 12, 1984 10 of 23
10
Figure 2. The U.S. sample: fits and misfits between big data and BDAC (N = 477).
We used ordinary least squares (OLS) dummy regression to test the effect of two fits and two
misfits on sustainable innovativeness. Proc Reg of SAS 9.4 was used to provide estimates. As four
independent variables (two fits and two misfits) represent four dummy variables, option “noint” was
included in the model statement of the “Proc Reg” to exclude the intercept term in the “Proc Reg”
estimations. The estimated coefficients were the effects of fits and misfits on sustainable
innovativeness under four scenarios. To test the three hypotheses, we used the “TEST” statement of
the “Proc Reg Model” to examine whether or not the coefficients estimated in the model were
significantly different from each other as hypothesized. We tested for possible differences of all six
possible pairs and the results were all significant (p < 0.01).
Table 3 displays the final estimates. The results in Table 3 indicate that both fits and misfits have
significant positive impact on the sustainable innovativeness of NSD projects in the United States.
The results from six paired-wise tests indicate that these effects differ from each other (p < 0.01). To
examine whether or not each hypothesis is supported, we use the standardized estimates and the
results of the paired-wise tests.
As predicted by H1, the effect of high-high fit on sustainable innovativeness (b = 0.701; p < 0.01)
is the greatest. However, counter to H1, the positive effect of high-low misfit on sustainable
innovativeness (b = 0.400; p < 0.01) is greater than that of low-low fit (b = 0.384; p < 0.01). Thus, H1 is
only partially supported by the data.
The results suggest that the effect of high-high fit on sustainable innovativeness (b = 0.701; p <
0.01) is significantly higher than that of low-low fit (b = 0.384; p < 0.01). Thus, as predicted by H2,
high-high fit increases sustainable innovativeness more than low-low fit does (p < 0.01). The data
provide supports for H2.
H3 predicts that low-high misfit improves sustainable innovativeness more than high-low misfit
does. Counter to H3, the results in Table 3 indicate that the effect of low-high misfit on sustainable
innovativeness (b = 0.340; p < 0.01) is significantly lower, not higher (as hypothesized by H3), than
that of high-low misfit (b = 0.400; p < 0.01). Thus, H3 is not supported by the U.S. data.
Table 3. The U.S. sample: results of dummy regression analysis (N = 477).
Dependent Variable: Sustainable Innovativeness
Parameter Estimate (β) Standard Error (S.E.) Standardized Estimate (b)
Figure 2. The U.S. sample: fits and misfits between big data and BDAC (N = 477).
We used ordinary least squares (OLS) dummy regression to test the effect of two fits and two
misfits on sustainable innovativeness. Proc Reg of SAS 9.4 was used to provide estimates. As four
independent variables (two fits and two misfits) represent four dummy variables, option “noint” was
included in the model statement of the “Proc Reg” to exclude the intercept term in the “Proc Reg”
estimations. The estimated coefficients were the effects of fits and misfits on sustainable innovativeness
under four scenarios. To test the three hypotheses, we used the “TEST” statement of the “Proc Reg
Model” to examine whether or not the coefficients estimated in the model were significantly different
from each other as hypothesized. We tested for possible differences of all six possible pairs and the
results were all significant (p < 0.01).
Table 3 displays the final estimates. The results in Table 3 indicate that both fits and misfits have
significant positive impact on the sustainable innovativeness of NSD projects in the United States. The
results from six paired-wise tests indicate that these effects differ from each other (p < 0.01). To examine
whether or not each hypothesis is supported, we use the standardized estimates and the results of the
paired-wise tests.
Table 3. The U.S. sample: results of dummy regression analysis (N = 477).
Dependent Variable: Sustainable Innovativeness
Parameter Estimate (β) Standard Error (S.E.) Standardized Estimate (b)
High-Low Misfit 6.118 *** 0.206 0.400
High-High Fit 6.963 *** 0.134 0.701
Low-Low Fit 4.179 *** 0.146 0.384
Low-High Misfit 5.380 *** 0.213 0.340
Model F-value 1263.050 ***
R-square 0.914
Adjusted R-square 0.914
Note: *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC; High-High
Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC;
Low-High Misfit = the misfit between low big data and high BDAC. The six paired-wise tests indicate that all pairs
are significantly different from each other at p < 0.01 (one-tailed test).
Sustainability 2020, 12, 1984 11 of 23
As predicted by H1, the effect of high-high fit on sustainable innovativeness (b = 0.701; p < 0.01) is
the greatest. However, counter to H1, the positive effect of high-low misfit on sustainable innovativeness
(b = 0.400; p < 0.01) is greater than that of low-low fit (b = 0.384; p < 0.01). Thus, H1 is only partially
supported by the data.
The results suggest that the effect of high-high fit on sustainable innovativeness (b = 0.701; p < 0.01)
is significantly higher than that of low-low fit (b = 0.384; p < 0.01). Thus, as predicted by H2, high-high
fit increases sustainable innovativeness more than low-low fit does (p < 0.01). The data provide
supports for H2.
H3 predicts that low-high misfit improves sustainable innovativeness more than high-low misfit
does. Counter to H3, the results in Table 3 indicate that the effect of low-high misfit on sustainable
innovativeness (b = 0.340; p < 0.01) is significantly lower, not higher (as hypothesized by H3), than that
of high-low misfit (b = 0.400; p < 0.01). Thus, H3 is not supported by the U.S. data.
4.2. Empirical Study 2: China
4.2.1. Measurement Validation in Empirical Study 2
As reported in Hao et al. [2], all measures were translated into Chinese using the double-translation
method [2] using four translators. Minor differences were discussed and resolved. Two pretests were
performed to evaluate the appropriateness of formats and accuracies using the participants of the
earlier interviewees. After pretests, minor modifications were made to formatting and wordings to
create the final survey [2].
4.2.2. Data
As reported in Hao et al. [2], to ensure comparability with the sample of the United States,
524 companies listed in the Small and Medium Enterprise and Growth Enterprise Market Boards of
the Shenzhen Stock Exchange in China were chosen as initial sampling frame. These companies were
further reduced to 482 companies to match with the sample from the United States after deleting all
companies with missing data. The details of the data collection were reported in [2]. This study used
all 632 NSD projects from the dataset. The final data included 40 from hotel, traveling, and tourism
services; 217 from banking, insurances, securities, financial investments, and related activities; 120 from
information and semiconductor; 91 from Internet-related services; and 164 from health care services [2].
4.2.3. Analysis and Results
Table 4 shows the descriptive statistics and correlation coefficient matrix of each variable for the
Chinese sample. The values on the diagonal are the Cronbach’s alpha coefficients of each variable,
all of which are greater than 0.7, indicating high reliability of our study measures. To ensure the
cross-national comparability of the data between China and the United States, we retained the same
measurement items for factor analysis as in the U.S. analysis. Table 5 shows the factor loadings of each
variable, which are all greater than 0.6, indicating high structural validity of the measurement items.
Table 4. The Chinese sample: descriptive statistics and correlation coefficient matrix (N = 632).
Innovativeness Big Data BDAC
Innovativeness 0.869
Big Data 0.588 *** 0.894
BDAC 0.389 *** 0.506 *** 0.767
Mean 5.297 4.571 6.254
S.D. 2.192 2.585 2.085
Note: *** p < 0.01 (two-tailed test). BDAC = Big data analytics capability. The Cronbach’s alpha for each scale is on
the diagonal in italics; the intercorrelations among the variables are on the off diagonal.
Sustainability 2020, 12, 1984 12 of 23
Table 5. The Chinese sample: factor loadings from exploratory factor analysis (N = 632).
Measure Items Innovativeness Big Data BDAC
INNO 1 0.819 0.242 0.070
INNO 3 0.811 0.238 0.049
INNO 5 0.743 0.224 0.194
INNO 4 0.735 0.180 0.191
INNO 2 0.728 0.211 0.149
Big Data 1 0.243 0.865 0.159
Big Data 2 0.241 0.797 0.275
Big Data 3 0.252 0.767 0.210
Big Data 4 0.377 0.752 0.200
BDAC 1 0.035 0.237 0.800
BDAC 2 0.175 0.023 0.762
BDAC 5 0.066 0.241 0.730
BDAC 4 0.285 0.237 0.622
Note: BDAC = Big data analytics capability. Bold numbers indicate items that load highly for the corresponding factor.
Following analysis of the U.S. sample, we used the mean values of big data and BDAC to divide
the sample of Chinese NSD projects into four scenarios: two fits (high-high fit and low-low fit) and
two misfits (high-low misfit and low-high misfit), as shown in Figure 3.
12
Note: *** p < 0.01 (two-tailed test). BDAC = Big data analytics capability. The Cronbach’s alpha for each
scale is on the diagonal in italics; the intercorrelations among the variables are on the off diagonal.
Table 5. The Chinese sample: factor loadings from exploratory factor analysis (N = 632).
Measure Items Innovativeness Big Data BDAC
INNO 1 0.819 0.242 0.070
INNO 3 0.811 0.238 0.049
INNO 5 0.743 0.224 0.194
INNO 4 0.735 0.180 0.191
INNO 2 0.728 0.211 0.149
Big Data 1 0.243 0.865 0.159
Big Data 2 0.241 0.797 0.275
Big Data 3 0.252 0.767 0.210
Big Data 4 0.377 0.752 0.200
BDAC 1 0.035 0.237 0.800
BDAC 2 0.175 0.023 0.762
BDAC 5 0.066 0.241 0.730
BDAC 4 0.285 0.237 0.622
Note: BDAC = Big data analytics capability. Bold numbers indicate items that load highly for the
corresponding factor.
Following analysis of the U.S. sample, we used the mean values of big data and BDAC to divide
the sample of Chinese NSD projects into four scenarios: two fits (high-high fit and low-low fit) and
two misfits (high-low misfit and low-high misfit), as shown in Figure 3.
Figure 3. The Chinese sample: fits and misfits between big data and BDAC (N = 632).
We used OLS dummy regression analysis to test the impacts of the two fits and the two misfits
on sustainable innovativeness. Table 6 shows the results of dummy regression analysis. To test the
three hypotheses, we used the “TEST” statement of the “Proc Reg Model” to examine whether or not
the coefficients estimated in the model were significantly different from each other as hypothesized.
We tested for possible differences of all six possible pairs and the results were all significant (p<0.01).
Figure 3. The Chinese sample: fits and misfits between big data and BDAC (N = 632).
We used OLS dummy regression analysis to test the impacts of the two fits and the two misfits on
sustainable innovativeness. Table 6 shows the results of dummy regression analysis. To test the three
hypotheses, we used the “TEST” statement of the “Proc Reg Model” to examine whether or not the
coefficients estimated in the model were significantly different from each other as hypothesized. We
tested for possible differences of all six possible pairs and the results were all significant (p<0.01).
Sustainability 2020, 12, 1984 13 of 23
Table 6. The Chinese sample: results of dummy regression analysis (N = 632).
Dependent Variable: Sustainable Innovativeness
Parameter Estimate (β) Standard Error (S.E.) Standardized Estimate (b)
High-Low Misfit 5.660 *** 0.224 0.329
High-High Fit 6.748 *** 0.128 0.688
Low-Low Fit 4.130 *** 0.126 0.427
Low-High Misfit 4.653 *** 0.169 0.360
Model F-value 1315.420 ***
R-square 0.893
Adjusted R-square 0.893
Note: *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC; High-High
Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC;
Low-High Misfit = the misfit between low big data and high BDAC. The six paired-wise tests indicate that all pairs
are significantly different from each other at p < 0.01 (one-tailed test).
Our results show that both fits and misfits between big data and BDAC have significant positive
impacts on sustainable innovativeness in China. The results from six paired-wise tests indicate that
these effects differ from each other (p < 0.01). To examine whether or not each hypothesis is supported,
we use the standardized estimates and the results of the paired-wise tests.
Results in Table 6 indicate that the positive effects of high-high fit (b = 0.688; p < 0.01) and low-low
fit (b = 0.427; p < 0.01) on sustainable innovativeness are greater than for high-low misfit (b = 0.329; p
< 0.01) and low-high misfit (b = 0.360; p < 0.01). Therefore, when there is a fit between big data and
BDAC, NSD projects can achieve higher sustainable innovativeness. Thus, H1 is supported by the
Chinese data.
Consistent with H2, the effect of high-high fit (b = 0.688; p < 0.01) on sustainable innovativeness is
higher than that of low-low fit (b = 0.427; p < 0.01), indicating that NSD projects with high levels of
both big data and BDAC can achieve higher sustainable innovativeness. Thus, H2 is also supported by
the data.
As predicted by H3, the positive effect of low-high misfit (b = 0.360; p < 0.01) on sustainable
innovativeness is greater than that of high-low misfit (b = 0.329; p < 0.01). Therefore, H3 is also
supported by the Chinese data.
4.3. Empirical Study 3: Singapore
4.3.1. Measurement Validation
To collect data in Singapore, we used the same measurement items as for the U.S. sample. As in
the Chinese sample, we distributed the study survey to 42 executives to conduct a pretest to ensure
that the expression of each item would be accurately understood by the participants in Singapore. We
made minor modifications on the formatting of the survey based on their feedback.
4.3.2. Data
To ensure comparability with the U.S. and China sample, companies were selected from the
Singapore Stock Exchange and supplemented with a list of members of four business associations in
Singapore. The data collection procedures described in the U.S. sample were adopted in Singapore.
We ultimately collected complete data for 294 NSD projects: 14 NSD in hotel, traveling, and tourism
services; 102 NSD in banking, insurances, securities, financial investments, and related activities; 62
NSD in information and semiconductor; 46 NSD in Internet-related services; and 70 NSD in health
care services.
Sustainability 2020, 12, 1984 14 of 23
4.3.3. Analysis and Results
The same data analyses are used to analyze the Singapore data. Table 7 shows the descriptive
statistics and correlation coefficient matrix of each variable for the Singapore sample. The values on the
diagonal are the Cronbach’s alpha coefficient for each variable, all of which are above 0.7, confirming
the high validity of our study measures. We also conducted factor analysis of the scale items. As shown
in Table 8, all factor loadings are between 0.641 and 0.884, indicating high structural validity of our
measurement scale.
Table 7. The Singaporean sample: descriptive statistics and correlation coefficient matrix (N = 294).
Innovativeness Big Data BDAC
Innovativeness 0.881
Big Data 0.566 *** 0.915
BDAC 0.393 *** 0.521 *** 0.775
Mean 4.298 3.430 6.353
S.D. 2.184 2.507 2.167
Note: *** p < 0.01 (two-tailed test). BDAC = Big data analytics capability. The Cronbach’s alpha for each scale is on
the diagonal in italics; the intercorrelations among the variables are on the off diagonal.
Table 8. The Singaporean sample: factor loading of variables (N = 294).
Measure Items Innovativeness Big Data BDAC
Innovativeness INNO 1 0.854 0.249 0.117
INNO 3 0.850 0.123 0.002
INNO 2 0.778 0.116 0.222
INNO 4 0.700 0.335 0.105
INNO 5 0.679 0.397 0.199
Big Data Big Data 1 0.214 0.884 0.168
Big Data 2 0.273 0.842 0.205
Big Data 4 0.280 0.831 0.197
Big Data 3 0.243 0.744 0.225
BDAC BDAC 1 0.058 0.240 0.817
BDAC 2 0.096 0.002 0.743
BDAC 4 0.302 0.242 0.703
BDAC 5 0.062 0.413 0.641
Note: BDAC = Big data analytics capability. Bold numbers indicate items that load highly for the corresponding factor.
Following Study 1 and 2, we used the mean values of big data and BDAC to divide the Singapore
sample into fits (high-high fit and low-low fit) and misfits (high-low misfit and low-high misfit)
categories as shown in Figure 4.
We then used OLS dummy regression analysis to test the impacts of the fits and misfits between
big data and BDAC on sustainable innovativeness. To test the three hypotheses, we used the “TEST”
statement of the “Proc Reg Model” to examine whether or not the coefficients estimated in the model
were significantly different from each other as hypothesized. The results shown in Table 9 reveal
that the fits and misfits between big data and BDAC have significant positive impacts on sustainable
innovativeness. The results from six paired-wise tests indicate that these effects differ from each other
(p < 0.10).
Sustainability 2020, 12, 1984 15 of 23
15
Figure 4. The Singaporean sample: fits and misfits between big data and BDAC (N = 294).
Table 9. The Singaporean sample: results of dummy regression analysis (N = 294).
Dependent Variable: Sustainable Innovativeness
Parameter Estimate (β) Standard Error (S.E.) Standardized Estimate (b)
High-Low Misfit 5.144 *** 0.426 0.264
High-High Fit 6.091 *** 0.195 0.684
Low-Low Fit 3.215 *** 0.177 0.399
Low-High Misfit 3.642 *** 0.196 0.406
Model F-value 449.170 ***
R-square 0.861
Adjusted R-
square
0.859
Note: *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC;
High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big
data and low BDAC; Low-High Misfit = the misfit between low big data and high BDAC. The six
paired-wise tests indicate that all pairs are significantly different from each other at p < 0.10 (one-
tailed test).
To examine whether or not each hypothesis is supported, we used the standardized estimates
and the results of the paired-wise tests. The results in Table 9 indicate that high-high fit (b = 0.684; p
< 0.01) has the greatest impact on sustainable innovativeness. However, counter to H1, the positive
effect of low-low fit (b = 0.399; p < 0.01) on sustainable innovativeness is lower, not higher, than that
of low-high misfit (b = 0.406; p < 0.01). Thus, H1 is only partially supported by the Singapore data.
We further find that the effect of high-high fit (b = 0.684; p < 0.01) on sustainable innovativeness
is greater than that of low-low fit (b = 0.399; p < 0.01), indicating that H2 is supported by the Singapore
data.
The date also shows that as predicted by H3, the effect of low-high misfit (b = 0.406; p < 0.01) on
sustainable innovativeness is greater than that of high-low misfit (b = 0.264; p < 0.01). Thus, H3 is
supported by the Singaporean data.
4.4. Summary of Hypothesis Testing for All Three Empirical Studies
Figure 4. The Singaporean sample: fits and misfits between big data and BDAC (N = 294).
Table 9. The Singaporean sample: results of dummy regression analysis (N = 294).
Dependent Variable: Sustainable Innovativeness
Parameter Estimate (β) Standard Error (S.E.) Standardized Estimate (b)
High-Low Misfit 5.144 *** 0.426 0.264
High-High Fit 6.091 *** 0.195 0.684
Low-Low Fit 3.215 *** 0.177 0.399
Low-High Misfit 3.642 *** 0.196 0.406
Model F-value 449.170 ***
R-square 0.861
Adjusted R-square 0.859
Note: *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC; High-High
Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC;
Low-High Misfit = the misfit between low big data and high BDAC. The six paired-wise tests indicate that all pairs
are significantly different from each other at p < 0.10 (one-tailed test).
To examine whether or not each hypothesis is supported, we used the standardized estimates
and the results of the paired-wise tests. The results in Table 9 indicate that high-high fit (b = 0.684;
p < 0.01) has the greatest impact on sustainable innovativeness. However, counter to H1, the positive
effect of low-low fit (b = 0.399; p < 0.01) on sustainable innovativeness is lower, not higher, than that of
low-high misfit (b = 0.406; p < 0.01). Thus, H1 is only partially supported by the Singapore data.
We further find that the effect of high-high fit (b = 0.684; p < 0.01) on sustainable innovativeness
is greater than that of low-low fit (b = 0.399; p < 0.01), indicating that H2 is supported by the
Singapore data.
The date also shows that as predicted by H3, the effect of low-high misfit (b = 0.406; p < 0.01) on
sustainable innovativeness is greater than that of high-low misfit (b = 0.264; p < 0.01). Thus, H3 is
supported by the Singaporean data.
4.4. Summary of Hypothesis Testing for All Three Empirical Studies
Table 10 summarizes the results of the six paired-wise tests for three empirical studies. The results
suggest the following results of the effects of fits and misfits on innovativeness:
Sustainability 2020, 12, 1984 16 of 23
1. In the United States, high-high fit > high-low misfit > low-low fit > low-high misfit (p < 0.01).
Therefore, H1 is partially supported because low-low fit < high-low misfit (not > as predicted by
H1); and H2 is supported. However, counter to H3, the effect of low-high misfit fit on sustainable
innovativeness is less, not higher (as predicted by H3), than High-Low Misfit is.
2. In China, high-high fit > low-low fit > low-high misfit > high-low misfit (p < 0.01). Therefore,
all three hypotheses are supported as predicted.
3. In Singapore, high-high fit > low-high misfit > low-low fit > high-low misfit (p < 0.10). Therefore,
H1 is partially supported because low-low fit < low-high misfit (not > as predicted by H1); and
both H2 and H3 are supported.
Table 10. Summary results of three hypotheses in three countries.
Hypothesis Pair Comparison
The United
States
(N = 477)
China
(N = 632)
Singapore
(N = 294)
H1 (fits > misfits) High-High Fit > Low-High Misfit 39.680 *** 98.070 *** 78.300 ***
High-High Fit > High-Low Misfit 11.860 *** 17.760 *** 4.070 **
Low-Low Fit > Low-High Misfit 21.640 *** 6.180 *** 2.620 * (<)
Low-Low Fit > High-Low Misfit 59.020 *** (<) 35.350 *** 17.480 ***
H2 (HH > LL) High-High Fit > Low-Low Fit 197.290 *** 212.910 *** 119.450 ***
H3 (LH > HL) Low-High Misfit > High-Low Misfit 6.220 *** (<) 12.860 *** 10.240 ***
Note: Numbers in Table 10 are F-statistics. (<) indicates that the effect is “less, not higher as predicted by the
hypothesis”. * p < 0.10; ** p < 0.05; *** p < 0.01 (because all hypotheses are directional, one-tailed test is used).
High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big data
and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit between
low big data and high BDAC.
5. Cross-National Comparative Analyses
To explore the similarities and differences among our samples in the United States, China, and
Singapore, we summarize the standardized estimates of fits and misfits on sustainable innovativeness
in Table 11. The results suggest that a high level of big data matched with a high level of BDAC has
the greatest positive effect on sustainable innovativeness. The importance of the other three scenarios
differs across countries.
Table 11. Ranking of the standardized estimates of the effects of fits and misfits on
sustainable innovativeness.
Dependent Variable: Sustainable Innovativeness
Rank
The United States
(Standardized Estimate b)
China
(Standardized Estimate b)
Singapore
(Standardized Estimate b)
1 High-High Fit (0.701) High-High Fit (0.688) High-High Fit (0.684)
2 High-Low Misfit (0.400) Low-Low Fit (0.427) Low-High Misfit (0.406)
3 Low-Low Fit (0.384) Low-High Misfit (0.360) Low-Low Fit (0.399)
4 Low-High Misfit (0.340) High-Low Misfit (0.329) High-Low Misfit (0.264)
Note: High-Low Misfit = the misfit between high big data and low BDAC; High-High Fit = the fit between high big
data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC; Low-High Misfit = the misfit
between low big data and high BDAC.
In the United States, high-low misfit has a larger effect on sustainable innovativeness than
low-low fit and low-high misfit do. Low-high misfit has the least effect on sustainable innovativeness.
The significant differences are validated by the paired-wise tests (p < 0.01). Access to high big data
resources provides project leaders with rich information about markets, customers, and competitors
to inform innovation activities [19]. A low level of big data resources reduces project team’s ability
to accurately evaluate the market development and demand directions, resulting in misdirected
Sustainability 2020, 12, 1984 17 of 23
innovation activities and missed market opportunities. In addition, when big data is lacking, too
much BDAC can cause capacity redundancy and blur the focus of existing big data analysis, leading to
ineffective innovation activities.
In China, low-low fit has a larger impact on sustainable innovativeness than low-high misfit and
high-low misfit. Fits are better than misfits. Results of paired-wise tests in Table 10 suggest that the
differences are significant (p < 0.01). Thus, for NSD projects in China, it is important that the levels of
big data and BDAC be in alignment to support the improvement of sustainable innovativeness. When
there is high big data and low BDAC, projects are unable to meet the needs for data analysis, and
experience data overload and blind innovation.
In Singapore, a high level of BDAC can improve sustainable innovativeness: after high-high
fit, low-high misfit has the largest impact, followed by low-low fit and high-low misfit. Results of
paired-wise tests in Table 10 suggest that the differences are significant (p < 0.10). The effect of low-high
misfit on sustainable innovativeness is 1.538 times higher (0.406/0.264) than that of high-low misfit,
indicating that big data on its own is unlikely to be a source of competitive advantage for NSD projects
in Singapore [33], but a high level of BDAC can lead to effective mining and analysis of the available
big data to create benefits for NSD projects.
To further evaluate cross-national differences on how fits and misfits affect sustainable
innovativeness, we performed dummy regression analyses using pooled data of three countries.
The United States is the base case. Two country dummy variables (China and Singapore) and eight
interaction terms (country dummy variables multiply by four fits and misfits) were introduced into
the equation. Table 12 presents the results of the analyses. The four coefficient estimates for the four
interaction terms with China (or Singapore) as dummy variable show the differences between the
United States and China (or Singapore). The differences between China and Singapore can be evaluated
by using the sum of the coefficients (U.S. + China vs. U.S. + Singapore). We used “TEST” option in the
model statement of the “Proc Reg” to compare the estimates. We present the results in Table 13.
Table 12. Results of regression analysis using pooled data (N = 1403).
Dependent Variable: Sustainable Innovativeness
Independent Variables
Parameter Estimate
(β)
Standard Error
(S.E.)
Standardized Estimate
(b)
High-Low Misfit 6.118 *** 0.211 0.368
High-High Fit 6.963 *** 0.137 0.718
Low-Low Fit 4.179 *** 0.150 0.429
Low-High Misfit 5.380 *** 0.218 0.423
China × High-Low Misfit −0.458 0.304 −0.018
China × High-High Fit −0.215 0.185 −0.015
China × Low-Low Fit −0.049 0.194 −0.003
China × Low-High Misfit −0.727 *** 0.273 −0.038
Singapore × High-Low Misfit −0.974 ** 0.481 −0.019
Singapore × High-High Fit −0.873 *** 0.241 −0.038
Singapore × Low-Low Fit −0.963 *** 0.234 −0.046
Singapore × Low-High Misfit −1.738 *** 0.295 −0.075
Model F-value 1006.620 ***
R-square 0.897
Adjusted R-square 0.896
Note: ** p < 0.05; *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC;
High-High Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low
BDAC; Low-High Misfit = the misfit between low big data and high BDAC. China = 1 if the sample is Chinese; 0
otherwise. Singapore = 1 if the sample is Singaporean; 0 otherwise. The base case is the United States.
Sustainability 2020, 12, 1984 18 of 23
Table 13. Testing results of the cross-national differences between China and Singapore.
China Singapore
Does the Effect Differ?
(F-Statistics and Significant Level)
The Effect of High-Low Misfit The Effect of High-Low Misfit 1.130
The Effect of High-High Fit The Effect of High-High Fit 7.900 ***
The Effect of Low-Low Fit The Effect of Low-Low Fit 17.700 ***
The Effect of Low-High Misfit The Effect of Low-High Misfit 15.300 ***
Note: *** p < 0.01 (two-tailed test). High-Low Misfit = the misfit between high big data and low BDAC; High-High
Fit = the fit between high big data and high BDAC; Low-Low Fit = the fit between low big data and low BDAC;
Low-High Misfit = the misfit between low big data and high BDAC. Dummy variables: China = 1 if the sample is
Chinese, 0 if not; Singapore = 1 if the sample is Singaporean, 0 if not.
The results in Tables 12 and 13 suggest that the coefficients for interaction terms (for both China
and Singapore) are all negative and that the numbers are more negative in Singapore than in China.
Therefore, the effects of fits and misfits on innovativeness is highest in the U.S. than in China and in
Singapore. The results suggest following additional cross-national differences for each of the scenarios:
(1) For effect of high-low misfit on sustainable innovativeness, the effect is less (β = −0.974; p < 0.05),
in Singapore than in the U.S. There are no significant differences in the effect between U.S. and
China (p > 0.10) and between China and Singapore (p > 0.10).
(2) For effect of high-high fit on sustainable innovativeness, the effect is the largest in the U.S.
(β = 6.963), the same in China (−0.215) but it is not significantly different from the U.S. with
p > 0.10), and the smallest in Singapore (β = 6.963–0.873= 6.090; p < 0.01). The results in Table 12
suggest that the difference between U.S. and Singapore is significant (p < 0.01). The results in
Table 13 indicate that the difference between China and Singapore is significant (p < 0.01).
(3) For effect of low-low fit on sustainable innovativeness, the effect is also the highest in the U.S.
(β = 4.179), the same in China (−0.049 but it is not significantly different from the U.S. with
p > 0.10), and the lowest in Singapore (β = 4.179–0.963= 3.216; p < 0.01). The results in Table 12
suggest that the difference between U.S. and Singapore is significant (p < 0.01). The results in
Table 13 indicate that the difference between China and Singapore is significant (p < 0.01).
(4) For low-high misfit on sustainable innovativeness, the effect is the highest in the U.S. (β = 5.380),
second in China (β = 5.380–0.727 = 4.653) and lowest in Singapore (β = 5.380–1.738 = 3.642).
The differences are all significant (p < 0.01).
6. Conclusions, Implications, and Future Research
6.1. Conclusions
Based on the IPT, we developed a theoretical model for studying the differential effects of fits and
misfits between big data and BDAC on sustainable innovativeness. We investigated four scenarios
and their impacts on sustainable innovativeness in a three-country comparative study. We tested
for significant differences between six pairs of the combinations and between the three pairs of the
countries. The empirical results provided at least partial supports for all three hypotheses.
First, as predicted by Hypothesis 1, we found that in China the effect of fits between big data
and BDAC on sustainable innovativeness is always stronger than that of misfits. However, in the
United States and Singapore, we found that the effect of low-low fit on sustainable innovativeness is
lower than that of misfits, indicating that the effect of fits between big data and BDAC on sustainable
innovativeness is not always stronger than that of misfits in these countries. This finding challenges
the assertions of previous research that fit between information, and information processing capability
is necessary to obtain value for the firm [4,7].
Second, as hypothesized in H3, across all three countries, we found that the positive impact of
high-high fit on sustainable innovativeness is greater than that of low-low fit. This finding supports the
conclusions of previous research that a high level of big data is a high-quality resource that can be fully
Sustainability 2020, 12, 1984 19 of 23
interpreted with a high level of BDAC to provide NSD project managers with insights into markets
and customers and thereby ensure the development of successful service products [10,19,30,33]. Our
finding that high levels of big data and BDAC can maximize sustainable innovativeness thus adds to
the results of Hao et al. [2], who suggested that when big data is high, improving BDAC will inhibit
innovation performance.
Third, we found significant differences in the impact of low-high misfit and high-low misfit
on sustainable innovativeness across the three countries. In the United States, the positive impact
of high-low misfit on sustainable innovativeness is higher than that of low-high misfit. This result,
consistent with Tan and Zhan [3], shows that rich big data resources can provide more sufficient,
reliable, and relevant information to guarantee the success of NSD projects even if BDAC is insufficient
to fully exploit these resources. Contrary to Song et al. [38], who found that the level of marketing
and R&D resources has an insignificant relationship with product innovativeness, we found that if
U.S. firms pursuing NSD projects lack big data resources, they cannot accurately obtain the valuable
information needed to ensure the sustainable innovativeness of service products. In contrast, in China
and Singapore, the impact of high-low misfit on sustainable innovativeness is less, not greater, than
that of low-high misfit. This result suggests that firms in China and in Singapore should operate
differently from firms in the U.S. They need to focus on increasing big data rather than BDAC to
successfully develop innovative service products. As Rialti et al. [35], Gupta and George [10], and
Ferraris et al. [11] have also found, even if there are limited big data resources, increasing BDAC
can enable project leaders to integrate and internalize existing big data information to improve the
sustainable innovativeness of projects.
Finally, the results from cross-national comparative analyses reveal four major conclusions. First,
the fits have greater effect on sustainable innovativeness in the U.S. and in China than that in Singapore.
Second, the impact of high-low misfit on sustainable innovativeness is higher in the U.S. than in
Singapore. Third, the positive effect of low-high misfit on sustainable innovativeness is the largest
in the U.S., followed by China, and then by Singapore. The possible reasons may be that there are
differences in the development speed of big data and analytics capability among the three countries.
Firms in the U.S. are better with applying big data and BDAC to develop innovative services and
products than firms in China and in Singapore are.
6.2. Theoretical Implications
This research enriches the literature on big data and innovation in several ways. First, this study
expands the application of the IPT with regard to big data. Previous studies on the IPT have focused on
firms’ need for traditional information sources and information processing capability [21,24]. However,
in the current marketplace, the need for information is largely affected by big data, which necessitates
higher information processing capability [19]. This study specifically considers big data and BDAC,
explores the application of the IPT in the context of big data and service innovation, and complements
existing research on the IPT [23,24].
Although other scholars such as Isik [4] have discussed the need for big data and information
processing capability and stressed the importance of their alignment to generate value from big data,
they have neither specified measurement items for these constructs nor conducted in-depth empirical
tests. Thus, this study fills these gaps in the empirical analysis of big data and BDAC by using fieldwork
and case studies to refine the definitions and connotations of big data and BDAC, improving existing
measurement scales, and proposing systematic measurement scales [14]. This study is also the first to
consider both fits and misfits between big data and BDAC and assess their impacts on sustainable
innovativeness. This not only enhances the previous research focusing only on the impact of big data or
BDAC [3,14,16,19] but also contributes to research on sustainable innovativeness [18] by demonstrating
the important impact of different configurations of fit between big data and BDAC in the context of
service innovation.
Sustainability 2020, 12, 1984 20 of 23
Finally, this study enriches the theory of cross-national big data management. Previous research
on big data and BDAC has mostly focused on the data of a single country [3,17,35]. In this study we
conducted a comparative analysis across three countries. By analyzing the data from NSD projects
in the United States, China, and Singapore, we explored the similarities and differences of fits and
misfits between big data and BDAC in the process of service innovation in these countries, building
the literature in this area.
6.3. Managerial Implications
The results of our analysis of the impact of fits and misfits between big data and BDAC on
sustainable innovativeness offer targeted recommendations for project managers in the different
countries to achieve successful service innovation.
First, when there are sufficient resources available, NSD project managers in the United States,
China, and Singapore should all invest in both big data and BDAC to improve sustainable innovativeness.
It is important that managers ensure the synchronous improvement of both big data and BDAC and
not emphasize the development of one aspect over the other.
Second, if resources are limited, then the recommended development strategies for project
managers differ among the three countries.
NSD project managers in the United States should invest in large amounts of high-quality big
data to ensure that the project always has a high level of big data resources to serve as the foundation
of the project. Project managers can improve their big data resources in four ways: (1) increase the
quantity and stock of big data as much as possible and constantly update the existing data to ensure its
timeliness so team members can understand changing market conditions and make timely adjustments
to the project; (2) build a data warehouse or mart to integrate various internal and external sources
of big data (e.g., customer demand, market development trends, business processing, competitor
information, etc.) and create a comprehensive knowledge base; (3) invest sufficient funds in NSD
projects so they can be fully developed; and (4) allocate time for effective analysis of big data to ensure
retention of reliable and relevant information, avoid decision-making mistakes, and achieve successful
project outcomes.
In China, managers can improve sustainable innovativeness by ensuring that big data and BDAC
maintain a balanced level. For example, if an NSD project has less big data, it should not invest in
further improving analysis tools and technologies but instead should focus on in-depth analysis of
existing data.
In Singapore, NSD project managers should focus on improvement of BDAC by investing in
pertinent analysis technologies and tools to enhance the ability of the project team to transform big data
into useful information. Managers can improve BDAC in three ways: (1) introduce advanced analysis
and algorithm tools, effectively analyze big data of different structure forms, extract all information
related to development activities, and find the connection between different processes and activities;
(2) focus on predicting potential market opportunities and development trends from existing data
resources; and (3) recruit high-quality team members with strong analytical skills and provide regular
training to assist team members in adapting to the development of technology and analysis tools.
Overall, project managers need to build a data-driven culture in their firm that supports big data
thinking and improves the sensitivity and cognitive ability of employees with regard to data.
6.4. Limitations and Future Research
There are several shortcomings of this study that can be improved upon in future work. We
focused here only on sustainable innovativeness as an important indicator of service innovation
output. Future studies should also consider how fits and misfits affect the quality of new service
products, the adoption of new service products, and innovation speed. These are all important
sustainable competitive advantages for sustainable service development. Furthermore, our study
sample included only five industries. Future studies should collect more data in other industries to
Sustainability 2020, 12, 1984 21 of 23
assess the generalizability of the research conclusions. Although we gained valuable insight from
our analysis of data from the United States, China, and Singapore, future endeavors can be enhanced
with data from other countries, particularly those that represent a variety of economic and cultural
systems, to further enrich cross-national comparative research and contribute to the understanding of
the sustainability of new service development.
Author Contributions: M.S. and H.Z. share the first-authorship of this article. H.Z. is corresponding author.
Conceptualization, M.S., J.H., and H.Z.; methodology, M.S. and H.Z.; data curation, H.Z. and M.S.; writing—original
draft preparation, J.H., H.Z., and M.S., writing-review and editing, M.S., J.H., and H.Z.; funding acquisition, H.Z.
All authors have read and agreed to the authorship and content of the article. All authors have read and agreed to
the published version of the manuscript.
Funding: This research was funded by the Humanities and Social Science Project of the China Ministry of
Education under the grant with project title: “Breakthrough service innovation: effects of big data analytics and
AI capability”. The partial funding was also supported by the Natural Science Foundation of Shaanxi Province of
China, grant number 2018JQ7003.
Acknowledgments: The authors thank assistant editor of Sustainability and two anonymous reviewers for their
useful suggestions which improve the quality of this article. The literature review and hypothesis development
were based on the graduation thesis of Jinjin Heng.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.
Appendix A. Study Measures and Sources
Big Data (adopted from Gupta and George [10]). (0 = strongly disagree; 5 = neutral; 10 =
strongly agree)
(1) We have access to very large, unstructured, or fast-moving data for analysis.
(2) We integrate data from multiple internal sources into a data warehouse or mart for easy access.
(3) We integrate external data with internal to facilitate high-value analysis of our
business environment.
(4) Our big data analytics projects are adequately funded.
(5) * Our big data analytics projects are given enough time to achieve their objectives.
Big Data Analytics Capability (BDAC) (adopted from Hao et al. [2]).
(1) We have advanced tools (analytics and algorithms) to extract values of the big data. (0 = no
capability; 5 = median level; 10 = very high level of capability; adopted from Hao et al. [2], which
was derived from Dubey et al. [34]; Gupta and George [10]).
(2) Our capability to discover relationships and dependencies from the big data is: (0 = no capability;
5 = neutral; 10 = very high level of capability; adopted from Hao et al. [2], which was developed
based on field research).
(3) * Our capability to perform predictions of outcomes and behaviors from the big data is: (0 = no
capability; 5 = median level; 10 = very high level of capability; adopted from Hao et al. [2], which
was derived from Gupta and George [10]).
(4) Our capability to discover new correlations from the big data to spot market demand trends and
predict user behavior is: (0 = no capability; 5 = median level; 10 = very high level of capability;
adopted from Hao et al. [2]; which was derived from Akter et al. [14]; Wamba et al. [33]).
(5) Our big data analytics staff has the right skills to accomplish their jobs successfully. (0 = none; 5 =
median level; 10 = very high level of capability; adopted from Hao et al. [2], which was derived
from Gupta and George [10]).
Sustainable Innovativeness (adapted from Song and Parry [37]). (Minor modifications were made based
on the pretests as reported in the text. The changes are shown below using the notations: deletion is
Sustainability 2020, 12, 1984 22 of 23
marked using
22
Sustainable Innovativeness (adapted from Song and Parry [37]). (Minor modifications were made
based on the pretests as reported in the text. The changes are shown below using the notations:
deletion is marked using deletion. Added text is marked with underline.) (0=strongly disagree;
5=neutral; 10=strongly agree).
(1) Our The products and services often incorporate innovative technologies which have never been
used in the industry before.
(2) Our The products and services caused significant changes in the whole industry.
(3) Our The products and services are one of the first of its kind introduced into the market.
(4) Our The products and services are highly innovative—totally new to the market.
(5) Our The products and services are perceived as most innovative in the industry.
Note: * indicates that the item was deleted based on factor analyses as described in the text.
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(3) The products and services are one of the first of its kind introduced into the market.
(4) The products and services are highly innovative—totally new to the market.
(5) The products and services are perceived as most innovative in the industry.
Note: * indicates that the item was deleted based on factor analyses as described in the text.
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Introduction
Theoretical Background and Framework
Information Processing Theory (IPT)
Big Data
Big Data Analytics Capability (BDAC)
Sustainable Innovativeness
Research Hypotheses
Methodology and Data Sources
Empirical Study 1: The United States
Measurement
Data
Analysis and Results
Empirical Study 2: China
Measurement Validation in Empirical Study 2
Data
Analysis and Results
Empirical Study 3: Singapore
Measurement Validation
Data
Analysis and Results
Summary of Hypothesis Testing for All Three Empirical Studies
Cross-National Comparative Analyses
Conclusions, Implications, and Future Research
Conclusions
Theoretical Implications
Managerial Implications
Limitations and Future Research
Study Measures and Sources
References
Motivation_to_use_
big_data_and
Motivation to use big data and big
data analytics in external auditing
Lina Dagilien_e and Lina Klovien_e
Kauno Technologijos Universitetas, Kaunas, Lithuania
Abstract
Purpose – This paper aims to explore organisational intentions to use Big Data and Big Data Analytics
(BDA) in external auditing. This study conceptualises different contingent motivating factors based on prior
literature and the views of auditors, business clients and regulators regarding the external auditing practices
and BDA.
Design/methodology/approach – Using the contingency theory approach, a literature review and 21 in-
depth interviews with three different types of respondents, the authors explore factors motivating the use of
BDA in external auditing.
Findings – The study presents a few key findings regarding the use of BD and BDA in external auditing.
By disclosing a comprehensive view of current practices, the authors identify two groups of motivating
factors (company-related and institutional) and the circumstances in which to use BDA, which will lead to the
desired outcomes of audit companies. In addition, the authors emphasise the relationship of audit companies,
business clients and regulators. The research indicates a trend whereby external auditors are likely to focus
on the procedures not only to satisfy regulatory requirements but also to provide more value for business
clients; hence, BDA may be one of the solutions.
Research limitations/implications – The conclusions of this study are based on interview data
collected from 21 participants. There is a limited number of large companies in Lithuania that are open to co-
operation. Future studies may investigate the issues addressed in this study further by using different
research sites and a broader range of data.
Practical implications – Current practices and outcomes of using BD and BDA by different types of
respondents differ significantly. The authors wish to emphasise the need for audit companies to implement a
BD-driven approach and to customise their audit strategy to gain long-term efficiency. Furthermore, the most
challenging factors for using BDA emerged, namely, long-term audit agreements and the business clients’
sizes, structures and information systems.
Originality/value – The original contribution of this study lies in the empirical investigation of the
comprehensive state-of-the-art of BDA usage and motivating factors in external auditing. Moreover, the study
examines the phenomenon of BD as one of the most recent and praised developments in the external auditing
context. Finally, a contingency-based theoretical framework has been proposed. In addition, the research also
makes a methodological contribution by using the approach of constructivist grounded theory for the
analysis of qualitative data.
Keywords Big data, Contingent factors, Big data analytics, External auditing
Paper type Research paper
1. Introduction
In the past several years, the technology of Big Data (BD) has gained remarkably in
popularity within a variety of sectors, ranging from business and government to scientific
and research fields (Ajana, 2015). The area of accounting and auditing is not an exception, as
companies are confronted by an unprecedented level of semi-structured and unstructured
The authors are pleased to acknowledge comments on earlier version of the paper from delegates at
38th EAA Congress, Glasgow, April 2015.
MAJ
34,7
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Received 27 January 2018
Revised 5 July 2018
18 September 2018
21 November 2018
Accepted 13 December 2018
Managerial Auditing Journal
Vol. 34 No. 7, 2019
pp. 750-782
© EmeraldPublishingLimited
0268-6902
DOI 10.1108/MAJ-01-2018-1773
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0268-6902.htm
http://dx.doi.org/10.1108/MAJ-01-2018-1773
massive data, which companies have to use and manage to be innovative, effective and
competitive. On one hand, we can see excitement about BD emerging because of the
recognition of opportunities in various areas (Marshall et al., 2015; Verma and
Bhattacharyya, 2017; Vera-Baquero et al., 2015; Enget et al., 2017). On the other hand, the
concept of BD is still confused (for example, social media data or business data) (Connelly
et al., 2016; Harford, 2014) and quite vague in terms of the circumstances of use.
According to Wang and Cuthbertson (2015), the potentially important role played by BD
and Big Data Analytics (BDA) in innovative auditing practice is evident. Quite a few studies
have discussed and analysed broad areas of BD and BDA in external auditing by explaining
and providing a context for researchers, drawing their attention to it in terms of general
issues (Alles and Gray, 2016; Alles, 2015; Earley, 2015; Wang and Cuthbertson, 2015;
Arnaboldi et al., 2017; Connelly et al., 2016) and arguing that the use of BDA is appropriate
and valuable to ensure the audit quality (Dubey and Gunasekaran, 2015; Brown-Liburd
et al., 2015; Vasarhelyi et al., 2015). BDA may improve the efficiency and effectiveness of
financial statement audits (KPMG, 2017; Cao et al., 2015; Yoon et al., 2015; Gepp et al., 2018),
but additional competencies and technological capabilities are necessary to implement BDA
(KPMG, 2017; Enget et al., 2017; Dubey and Gunasekaran, 2015; Brown-Liburd et al., 2015;
Zhang et al., 2015; Appelbaum et al., 2017, 2018).
Nonetheless, auditing is lagging behind the other research streams in the use of valuable
BDA (Gepp et al., 2018). However, research on understanding the motives for using BDA is
limited, as current studies do not attempt to explain why audit companies should actually
use BDA. Hence, an external audit is analysed from two process points of view – the audit
process between the audit company and client, and the audit process between the audit
company and regulatory bodies. In fact, BD only became accessible recently through
powerful analytical tools, but there are no obvious institutional forces that use BD
information or to implement BDA at the corporate level. The problematisation proposed in
the paper is the result of a dialectical interrogation (Alvesson and Sandberg, 2011) of audit
companies, business clients and regulatory bodies and the domain of literature targeted to
challenge assumptions. The use of innovative analytical tools such as BDA may cause a
tension among audit companies, business clients and regulators. This aspect arises because
of interdependence in the auditing process.
The previous literature has stipulated several contingent factors (namely, company size,
strategic orientation, modern technologies and regulatory environment) that can strengthen
or pose challenges to the use of BDA in external auditing. We elaborate on different
operating factors, as underlying theoretical assumptions, relevant to consider their different
influences on different stages of financial auditing, including the actors in financial auditing.
Based on these assumptions, we raise the following research question:
RQ1. What factors influence the motivation to use BDA in external auditing and how
intensively are these factors expressed by audit companies, business clients and
regulators?
The main contributions of this paper are the following. To the best of our knowledge, we are
among the first to study the comprehensive state-of-the-art of BDA usage, the motivating
factors and the potential outcomes for audit companies empirically. We explain how
different institutional and company-related factors are expressed and influence the decision
of whether to use BDA in external auditing. In particular, we focus on the phenomenon of
BD in external auditing by observing the views of diverse participants (namely, audit
companies, audit clients and audit regulators). Prior literature that examined audit analytics
focussed mainly on single influencing factors without taking the entire contingency-based
Big data and
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751
view into account. This study investigates the use of BDA in external auditing from the
perspective of contingency theory. In addition, the study also makes a methodological
contribution by introducing the use of the constructivist grounded theory approach within
the context of a novel research question, for which the existing literature and data are
generally lacking.
The paper is organised as follows. The literature review and the theoretical framework
pertaining to BDA use in an external auditing are presented in Section 2 of this paper.
Section 3 presents the methodology used, while Section 4 presents the results and the
findings from the interviews. The discussion and conclusion are presented in Section 5 of
this paper. Research limitations and further research directions are also provided.
2. Literature review and theoretical framework
2.1 Literature review of big data analytics in external auditing
During the past few years, researchers have produced an impressive amount of general
reviews, conceptual and research papers in an attempt to define the concept of BD and data
analytic tools. The 3Vs (volume, variety and velocity) are the three best-known defining
dimensions of BD. Laney introduced the 3Vs concept in a 2001 MetaGroup research
publication, 3D data management: Controlling data volume, variety and velocity. In much of
the business research, BD is seen as a new opportunity to enhance productivity, efficiency
and innovativeness in companies (Sheng et al., 2017; Verma and Bhattacharyya, 2017;
Connelly et al., 2016; Marshall et al., 2015; Vera-Baquero et al., 2015; Ajana, 2015).
Overall, the emergence of BD is both promising and challenging for social research, as
well as for the accounting and auditing areas, which are regarded as intrinsically data-
intensive. According to Warren et al. (2015), BD will have increasingly important
implications for accounting ecosystems in all senses, even as new types of data become
accessible, as will the inherent technological paradoxes of BD and corporate reporting
(Al-Htaybat and Alberti-Alhtaybat, 2017; Bhimani and Wilcocks, 2014) and new
performance indicators based on BD (Arnaboldi et al., 2017).
In general, auditors work with structured financial data; however, the volume and
complexity of business companies require even more rapid and sophisticated information
and analyses of unstructured or semi-structured non-financial BD from both internal and
external sources. In external auditing, BD may be conceptualised as an additional
information resource that has a direct effect on the understanding about the environment of
the business client and the performance of an audit. Moreover, the inclusion of BD may
contribute to the development and evolution of effective BDA tools and changes in the audit
processes.
BDA is the process of inspecting, cleaning, transforming and modelling BD to discover
and communicate useful information and patterns, suggest conclusions and support
decision-making (Cao et al., 2015) by using “smart” algorithms (Davenport, 2014). According
to Wang and Cuthbertson (2015), the potential of BDA to improve the practice of auditing is
quite significant. A detailed literature review is commonly accepted as the beginning step in
research and is important to indicate relevant research in a field. Accordingly, this research
began with a literature review of the fields of BD, BDA and auditing. Research synthesis
was selected as the method for the literature review with the aim of using the existing
literature (Cooper et al., 2009; Dixon-Woods et al., 2005). The literature review outlines a few
main directions and possible influences of BDA in the context of auditing. A major research
stream in the field argues that use of BDA is useful and valuable for ensuring audit quality
(Cao et al., 2015; Dubey and Gunasekaran, 2015; Brown-Liburd et al., 2015; Yoon et al., 2015;
MAJ
34,7
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Vasarhelyi et al., 2015) by improving the efficiency and effectiveness of financial statement
audits and by using BD as audit evidence.
The second stream of research focusses on additional competences that are necessary to
ensure an effective process when using BDA (Dubey and Gunasekaran, 2015). The latest
research by McKinney et al. (2017); Enget et al. (2017); Janvrin and Weidenmier Watson
(2017) and Sledgianowski et al. (2017) emphasises the need to incorporate issues of BD and
BDA into the accounting curriculum by acknowledging that these technologies are
transforming the accounting profession (Enget et al., 2017; Fay and Negangard, 2017;
Brown-Liburd et al., 2015; Zhang et al., 2015).
The third stream of research emphasises the role of changes in auditing standards. On
one hand, Appelbaum et al. (2017) argued that the standards themselves do not forbid the
use of BDA, but that the economics of external audits make analytics more difficult or
nearly impossible to use. On the other hand, the key methodological problem is using BD as
audit evidence (Brown-Liburd and Vasarhelyi, 2015). According to the standards, BD
evidence should be considered as less reliable for audit evidence (Appelbaum, 2016). Hence,
changes in the methodological audit approach, a change in standards to focus on data, the
processes that generate them and the analysis thereof, changes in the nature of accounting
records and auditing domains will add value and relevance to the accounting profession
(KPMG, 2017; Krahel and Titera, 2015; Vasarhelyi et al., 2015; Gray and Debreceny, 2014).
Moreover, updated standards may help to overcome the auditing profession’s apparent
reluctance to engage with BDA (Gepp et al., 2018).
Finally, the fourth stream of research explains the technological challenges for
companies of using BDA, with the focus on continuous auditing technology (Rikhardssona
and Dull, 2016; Appelbaum et al., 2016; Sun et al., 2015; Chen et al., 2015; Alles, 2015; Chiu
et al., 2014) and BD techniques (Gepp et al., 2018; Appelbaum et al., 2017). Moreover,
according to the literature review, Appelbaum et al. (2018) classified the audit analytics used
in the various audit stages. As external auditing is inseparable from the characteristics of
business clients, Al-Htaybat and Alberti-Alhtaybat (2017) identified the inherent
technological paradoxes of using BD in corporate reporting.
According to the literature review, it could be stated that the main streams of research
focus on and disclose the outcomes and value of the use of BDA in external auditing, the
aspects that have an influence on the efficient use of BDA and discuss the interaction
between BD and traditional sources of data, as well as BD’s impact on audit judgement and
behavioural research. It could also be stated that the external conditions and the
environment have an influence on the use of BDA in external auditing. On the other hand,
the research could be described as fragmented, disclosing different but limited aspects that
motivate or challenge the use of BDA in external auditing and a complete list of motivation
factors influencing the use of BDA in external auditing has not been researched.
2.2 The theoretical framework
Contingency theory focusses on how elements must fit together to reach the desired
configuration and the forms of fit, as proposed by Venkatraman (1989). In fact,
the contingency-based approach that is used widely in management research (Chenhall, 2003;
Chapman, 1997; Ittner and Larcker, 1997) could be also applied to explain audit companies’
intentions to adopt analytical tools at the corporate level.
Considering the complexity and dynamism of the audit process, the necessity of using
BDA might be influenced by different, contingent, external and internal factors. Auditors
require access to documents, systems, policies and procedures to manage an audit. They
must remain compliant with accounting and auditing standards, government regulations
Big data and
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and internal requests. Audit teams may begin the audit process with meetings during which
they gain risk and control awareness. Auditors perform substantive procedures and test
controls, and then draft reports that they submit to management and regulatory authorities
(Davoren, 2016). Many contingency variables have been found to be relevant, including the
environment – in particular, environmental uncertainty and market competition (Otley,
2016), technology (Otley, 1980, 2016; Chenhall, 2003), national culture (Ahmad and
Schroeder, 2003; Flynn and Saladin, 2006; Otley, 2016), strategic context (Wickramasinghe
and Alawattage, 2007; Sila, 2007) and company size and structure (Otley, 2016;
Wickramasinghe and Alawattage, 2007). While it is possible that all these play an important
role in the design of control systems (Brivot et al., 2017), this paper focusses particularly on
the main contingent factors that have been subject to investigation, namely, the
environment, technology, strategic context, size and structure. The contingency of natural
culture has not been taken into consideration.
Environment, as a contingency factor, may constitute the market and its associated
factors, such as prices, products, competition, government policies, etc., (Wickramasinghe
and Alawattage, 2007). Environment (as a contingency) may constitute the audit market’s
uncertainty and its associated factors, such as audit fees, competition and regulators’
policies, such as the attitudes of those setting the standards (Li et al., 2018). Looking at the
BDA’s influence from the external auditing point of view, audit market regulators play a
particularly important role in ensuring audit companies’ public quality aspects and
enhancing the use of data analytic tools.
Technologies can be understood as the processes used by companies to convert inputs
into outputs (Khandwalla, 1977). When a company fails to match its technology to its
structure, it does not succeed as a sustained organisation (Wickramasinghe and Alawattage,
2007). In audit companies, technologies involve both knowledge and techniques. Moreover,
technology, as a contingent factor, refers to the so-called hard IT-related aspects adopted by
companies (Garengo and Bititci, 2007). Hence, BDA, as an IT tool, may have a direct impact
on the audit process by influencing the audit phase of engagement. BDA may have an
indirect impact on the audit planning phase, as audit strategies and audit plans are
developed according to the data and information coming from the analysis of client’s
environment. BDA, as an IT tool, may also have a direct influence on compliance and
substantive testing and on evaluations and reports. Overall, the need to use BDA may
depend on the requirements of the audit regulatory bodies and business clients and on
internal technological capabilities, IT-related managerial activities, such as the internal
investments in hardware and software, external consultants, etc., (Tarek et al., 2017).
Based on the notions of contingency theory, researchers have discussed how the fit
between environment and strategy can influence organisational performance. Thompson
(1967) argued that changes in technology and environmental factors resulted in differences
in structures, strategies and decision processes. Henderson and Mitchell (1997), Spanos and
Lioukas (2001) and Johnson and Scholes’ (2008) research results supported the argument
that strategy was one of the effects that had influence as a significant determinant of
performance. Pateli and Giaglis (2005) developed a structured approach to changing the
business model of a company (including strategy perspective), which introduced a
technological innovation by keeping the principles of the old (traditional) business logic and
taking the effects incurred from the firm’s internal and external environment into account.
With reference to contingency theory, it might be suggested that strategic orientation could
have a significant influence in persuading audit companies to use BDA in auditing process
in an attempt to find the fit among new trends in technology, the environment and
organisational strategy. Referring to contingency theory, one might suggest that strategic
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754
orientation could influence audit companies to use BDA in auditing process significantly. A
BD-based approach is inseparable from the corporate core strategy and aims. As suggested
by Gepp et al. (2018), long-term orientation towards the use of BDA may outline future
opportunities for auditing in the context of real-time information and on collaborative
platforms and in peer-to-peer marketplaces.
Size has also been found to be an important contingent factor in understanding the
nature of organisational structures and behaviour (Wickramasinghe and Alawattage, 2007;
Otley, 2016). This implies that audit companies need to pay attention to the size of the audit
company itself and to that of the business client when creating an audit strategy and plan.
According to contingency theory, large companies have extensive specialisation,
standardisation and formalisation, but these features are less important in small companies
(Wickramasinghe and Alawattage, 2007); thus, it could be stated that small clients might not
be able to provide all the necessary information as BD for further analysis and the
application of BDA tools. Furthermore, small audit companies might not be able to use BDA
for their business clients because of a lack of trained staff and limited technological
capabilities.
Structure refers to the establishment of certain relationships among people with specified
goals and tasks (Wickramasinghe and Alawattage, 2007). A poorly fitting structure is
nothing else but a waste of resources and leads to the ultimate collapse of the business
(Mintzberg, 1987; Otley, 2016). Accordingly, it could be stated that different methods,
instruments, functions and processes cannot be designed without finding the best structure
alignment. From a BDA point of view, it might be assumed that a suitable and organic
structure would be able to support the implementation of innovative analytical tools in audit
companies and vice versa.
The literature describes several factors that can strengthen or pose a challenge to the use
of BDA in external auditing by integrating them in a theoretical framework (Figure 1).
The theoretical framework contains key participants involved in the auditing process
(audit companies, business clients and regulators), the auditing process (where BDA might
appear in different phases of an audit) and the contingent factors discussed above.
3. Research methodology
Based on the literature review, we explored different contingent factors that may motivate
the use of BD and BDA in external auditing theoretically. Qualitative research (Birkinshaw
Figure 1.
Theoretical
framework for
influencing factors to
use BDA in external
auditing
Process
Influence
REGULATORY BODIES
AUDIT COMPANY
AUDIT PROCESS
BUSINESS CLIENT COMPANY
Contingent
factors:
Environment
Technology
Company size
Strategic
orientation
Structure
BD/A
Big data and
big data
analytics
755
et al., 2011) adopted the constructivist grounded theory approach as described by Charmaz
(2006, 2014) for two main reasons:
(1) BD and BDA are rarely researched phenomena within the field of auditing, and we
were motivated to understand “the actual production of meanings and concepts
used by social actors in real settings” (Gephart, 2004, p. 457).
(2) We aimed to develop theoretical insights into a process about which there is little
extant theorising or empirical knowledge (Suddaby, 2006).
This research uses the analysis approach suggested by Corbin and Strauss (1990) to present
rich and detailed descriptions, which allows the reader to make sufficient contextual
judgements to transfer the interview findings to alternative settings.
We followed the main stages in grounded theory research for qualitative data analysis
(McNabb, 2008; Corley, 2015), namely, collecting data, open coding, axial coding and
developing theoretical insights.
3.1 Data collection
The research on the motivation to use BDA in external audits was conducted using semi-
structured interviews to allow for follow-up questions. Interview questions derived from
theory are the tools used to obtain information that will help to answer the research question
(Glesne, 2006).
The respondents were selected on the basis of two considerations, namely, the company
and the respondent’s position. With regard to the first consideration, the companies that
were selected as the three case studies were selected an audit network company dealing with
DA, a business client company dealing with BD and a regulator. This selection was intended
to obtain different perspectives on the same phenomenon. Table I shows the description of
the sample.
For the second consideration, the respondents were selected according to their positions
in the company. Hence, the respondents were auditors and BD analysists working and
Table I.
Sample description
Cases/companies
Duration of recorded
interviews in minutes
Transcript
pages
No. of
interviews
Big 4 (1) 41.48 7 1
Big 4 (2) 43.04 8 1
Big 4 (3) 36.45 7 1
Big 4 (4) 42.32 7 1
International audit network 130.05 27 3
National audit network 47.37 11 1
Audit companies 340.71 67 8
Global financial services and IT company 105.53 24 5
Financial institution operating worldwide 90.41 20 2
National energy company 25.59 5 1
Business companies (clients) 221.53 49 8
Tax analytics 141.58 32 4
Audit controller 39.14 8 1
Regulators 180.72 40 5
Total 742.96 156 21
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dealing with the company’s data. The selection of the participants, as different stakeholders,
was also intended to improve the validity and reliability of the study (Yin, 2003) (Table II).
During the face-to-face interviews, which lasted for 35 min on average, the participants
were given a copy of the interview guide (questionnaire, see Appendix) to ensure sufficient
coverage of the research aim and the optimal use of time.
Part 1 was related to the background information and general understanding of BD in
the company and the motivating factors for using BDA. Part 2 was related to the practical
aspects of using BDA in the audit process. The proposed questions included “why” and
“how” information and the respondents were asked to discuss the reasons, motivations,
creation, implementation and use processes of BDA, including values, its challenges and the
possible changes for the auditing process.
The interviews were tape-recorded with prior permission from the participants after they
signed an official agreement. Towards the end of each interview, time was allowed for open
and informal discussions to extract information that participants might otherwise have been
reluctant to provide during the formal interview sessions. Overall, the interviews lasted for
12 h and 38 min, resulting in 156 pages of transcripts. The interviews were conducted in
Lithuanian or English. Data were collected and analysed in 2015-2017, except for the
interview with the BDA analyst from the audit company, which was conducted and
analysed in 2018.
3.2 The setting of the Lithuanian audit market
We focus next on the description of the setting of the Lithuanian audit market as a critical
factor for the analysis and interpretation of the data.
The Lithuanian audit market is relatively young and concentrated. In 2009, the National
Audit Standards were abandoned, and only the International Standards on Auditing (ISA)
have been applied since. According to the data from the Lithuanian Chamber of Auditors of
1 February 2017, 357 auditors and 170 audit companies have been certified, of which 141 out
of 170 audit companies were listed as very small companies, 25 audit companies as small
companies, 4 audit companies as medium companies and 1 audit company as large.
In 2015, Lithuanian audit companies conducted 4,217 audits in total, including 3,898
financial statement audits in Lithuania, 273 audits on consolidated financial statements in
Lithuania, 44 audits on interim financial statements in Lithuania and 2 audits abroad
(Lithuanian Chamber of Auditors Report, 2015). Among the clients of audit companies, the
current companies include public interest entities and companies that are legally required to
carry out audits but, in general, there are not many large clients.
The audit market in Lithuania is concentrated – the ten largest audit companies,
according to the received revenue from audit activities in 2015, accounted for almost 70 per
cent of the audit market. The average fee per audit performed in 2015 amounted to e414,304.
The highest average fee for one audit was for the companies in the Big 4 – e869,850, which is
four times higher than it was for audit companies with one or two auditors and three times
Table II.
Position of
respondents
Cases/companies
Auditors BD analytics
Senior Partner Field expert Head
Audit companies 4 3 � 1
Business client companies 1 � 3 4
Regulators 1 � � 4
Total 9 12
Big data and
big data
analytics
757
higher than it was for audit companies with three or more auditors (Lithuanian Chamber of
Auditors Report, 2015). However, given the fact that the audit companies for the Big 4 spend
most of their time on audits, the difference in the average fee for the audit service is lower.
Significant fluctuations in the fees for services between international and smaller national
audit companies are typical of the Lithuanian audit market. This situation can also be
explained by the fact that international networking audit companies are auditing the largest
and, at the same time, the most complex business companies.
3.3 Coding and analyses
Preliminary coding on the basis of the 21 interviews was developed first. After the
transcription of all the interviews was completed, all the transcripts were analysed by both
researchers separately via a systematic process of coding and categorisation intended to
group the information from the transcripts into similar concepts or themes that emerged
from the analysis. We then discussed the open coding of sentences or paragraphs within the
transcripts to identify key concepts emerging from the data and to link them to what
allowed agreeing on certain open codes. Table III illustrates the open coding of the interview
transcripts.
During the process of our further discussions and analyses, open codes were assigned to
broader categories, called second-order codes, which highlighted the relationships among
the open codes (Lee, 1999). These second-order codes were then used to create broader
categories – axial codes – to facilitate theoretical insights (Lee, 1999), such as current
practices, company factors, institutional factors and outcomes. Table IV shows the axial
codes and the descriptions thereof.
Coding process and codes, as a method of qualitative data analysis, (McNabb, 2008;
Corley, 2015) allowed for the identification of key concepts emerging from the qualitative
data – the transcripts. Meaningful results and findings are presented on the basis of axial
codes, which indicated the main groups of motivating factors for and the circumstances in
which to use BD and BDA in external auditing.
4. Results and findings
After careful consideration of the second-order and axial codes, “Current Practices” was
organised to include the open codes of experience, benefits, financial resources and
increasing trend, which were identified as having similarities based on their currently
existing features. During the data analysis process, the second-order and axial code
“institutional factors” was organised using open codes such as regulation system, market
structure and education. Three open codes, namely, strategic decisions, governance
structure and size were identified as a second-order code strategy-related factors and three
open codes, namely, information system (IS), competent teams and internal capabilities were
identified as a second-order code, “resource-related factors”. These two second-order codes
were then used to create a broader category, namely, the axial code “company factors”.
There were three open codes, which were planning, management and reporting, which were
integrated based on their properties in a second-order code, “internal control”. Five open
codes were understanding the client’s company, audit planning, audit performance and
conclusion and audit team and audit fee were identified as having similarities; thus, they
were combined in a second-order code, “audit process”. In addition, the open codes audit
quality and control of audit quality were combined in a second-order code, “quality”. These
three second-order codes were identified as having similarities, in the main areas that are
influenced by the use of BD/BDA in business and audit companies and were combined in an
axial code, “outcomes”.
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758
O
pe
n
co
de
s
D
ef
in
it
io
n
A
ud
it
co
m
pa
ni
es
B
us
in
es
s
co
m
pa
ni
es
T
ax
an
d
au
di
t
re
gu
la
to
rs
E
xp
er
ie
nc
e
In
fo
rm
at
io
n
re
la
ti
ng
to
th
e
co
nc
ep
t,
un
de
rs
ta
nd
in
g
an
d
du
ra
ti
on
of
us
in
g
B
D
/
B
D
A
in
a
co
m
pa
ny
D
is
cl
os
ed
a
D
is
cl
os
ed
D
is
cl
os
ed
w
it
h
an
or
ie
nt
at
io
n
to
w
ar
ds
th
e
fu
tu
re
St
ra
te
gi
c
de
ci
si
on
In
fo
rm
at
io
n
re
la
te
d
to
th
e
co
rp
or
at
e
st
ra
te
gy
an
d
to
p
m
an
ag
em
en
t’s
at
ti
tu
de
/
co
m
m
it
m
en
t
to
us
in
g
B
D
an
d
m
od
er
n
da
ta
an
al
yt
ic
to
ol
s
H
ig
h
im
po
rt
an
ce
c
H
ig
h
im
po
rt
an
ce
N
ot
di
sc
lo
se
db
G
ov
er
na
nc
e
st
ru
ct
ur
e
In
fo
rm
at
io
n
re
la
te
d
to
to
p
m
an
ag
em
en
t
-
go
ve
rn
m
en
t,
fo
re
ig
n
m
an
ag
em
en
t,
na
ti
on
al
sh
ar
eh
ol
de
rs
,g
lo
ba
ln
et
w
or
ki
ng
co
m
pa
ny
H
ig
h
im
po
rt
an
ce
H
ig
h
im
po
rt
an
ce
N
ot
di
sc
lo
se
d
IS
In
fo
rm
at
io
n
re
la
te
d
to
th
e
ov
er
al
lc
or
po
ra
te
in
fo
rm
at
io
n
sy
st
em
,i
nc
lu
di
ng
th
e
in
te
rn
al
co
nt
ro
ls
ys
te
m
,fi
na
nc
ia
la
cc
ou
nt
in
g
pr
og
ra
m
m
es
an
d
no
n-
fi
na
nc
ia
ld
at
a
pr
og
ra
m
m
es
,d
at
ab
as
es
an
d
so
ft
w
ar
e
us
ed
,
le
ve
lo
fc
om
pu
te
ri
sa
ti
on
of
bu
si
ne
ss
pr
oc
es
se
s
D
is
cl
os
ed
D
is
cl
os
ed
D
is
cl
os
ed
B
en
efi
ts
In
fo
rm
at
io
n
re
la
te
d
to
th
e
be
ne
fi
ts
of
B
D
A
,
in
cl
ud
in
g
ad
va
nt
ag
es
re
ce
iv
ed
,t
im
e
ef
fi
ci
en
cy
,m
on
ey
sa
vi
ng
s
an
d
va
lu
e
fo
r
so
ci
et
y
by
pr
ov
id
in
g
da
ta
th
at
ar
e
m
or
e
re
lia
bl
e
D
is
cl
os
ed
D
is
cl
os
ed
D
is
cl
os
ed
F
in
an
ci
al
re
so
ur
ce
s
In
fo
rm
at
io
n
re
la
te
d
to
co
st
s
of
cr
ea
ti
ng
an
d
im
pl
em
en
ti
ng
B
D
A
,i
nc
lu
di
ng
th
e
fi
na
nc
ia
l
re
so
ur
ce
s
ne
ed
ed
D
is
cl
os
ed
D
is
cl
os
ed
D
is
cl
os
ed
w
it
h
an
or
ie
nt
at
io
n
to
w
ar
ds
th
e
fu
tu
re
Si
ze
In
fo
rm
at
io
n
re
la
te
d
to
th
e
co
nd
it
io
ns
ne
ed
ed
to
co
lle
ct
an
d
im
pl
em
en
t
B
D
su
ch
as
th
e
au
di
t
co
m
pa
ny
’s
si
ze
an
d
th
e
cl
ie
nt
’s
si
ze
H
ig
h
im
po
rt
an
ce
,a
ud
it
co
m
pa
ny
’s
si
ze
H
ig
h
im
po
rt
an
ce
,
cl
ie
nt
’s
si
ze
N
ot
di
sc
lo
se
d
(c
on
ti
nu
ed
)
Table III.
Open codes derived
from different
interview transcripts
Big data and
big data
analytics
759
O
pe
n
co
de
s
D
ef
in
it
io
n
A
ud
it
co
m
pa
ni
es
B
us
in
es
s
co
m
pa
ni
es
T
ax
an
d
au
di
t
re
gu
la
to
rs
P
la
nn
in
g
In
fo
rm
at
io
n
re
la
te
d
to
th
e
de
ve
lo
pm
en
t
of
pl
an
ni
ng
an
d
fo
re
ca
st
in
g
pe
rf
or
m
an
ce
,
pr
oc
es
se
s
an
d
ac
ti
vi
ti
es
by
us
in
g
B
D
A
N
ot
di
sc
lo
se
d
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
U
nd
er
st
an
di
ng
th
e
cl
ie
nt
’s
co
m
pa
ny
In
fo
rm
at
io
n
re
la
te
d
to
un
de
rs
ta
nd
in
g
th
e
cl
ie
nt
’s
co
m
pa
ny
an
d
it
s
en
vi
ro
nm
en
t,
be
tt
er
ev
al
ua
ti
on
of
in
he
re
nt
ri
sk
s
an
d
th
e
co
nt
ro
lt
he
re
of
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
N
ot
di
sc
lo
se
d
A
ud
it
pl
an
ni
ng
In
fo
rm
at
io
n
re
la
te
d
to
th
e
pl
an
ni
ng
ac
ti
vi
ti
es
,p
re
pa
ra
ti
on
of
th
e
au
di
t
pl
an
an
d
au
di
t
pr
og
ra
m
m
es
by
us
in
g
B
D
A
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
N
ot
di
sc
lo
se
d
A
ud
it
pe
rf
or
m
an
ce
an
d
co
nc
lu
si
on
In
fo
rm
at
io
n
re
la
te
d
to
pe
rf
or
m
in
g
th
e
au
di
t,
th
e
ap
pl
ic
at
io
n
of
an
al
yt
ic
al
pr
oc
ed
ur
es
an
d
co
nt
ro
lt
es
ts
,p
ro
vi
di
ng
th
e
au
di
to
r’
s
op
in
io
n,
co
nc
lu
si
on
,c
on
ti
nu
ou
s
au
di
ti
ng
in
st
ea
d
of
on
a
sa
m
pl
e
ba
si
s
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
N
ot
di
sc
lo
se
d
R
ep
or
ti
ng
In
fo
rm
at
io
n
re
la
te
d
to
pr
ov
id
in
g
re
su
lt
s
ab
ou
tt
he
co
m
pa
ny
in
th
e
re
po
rt
to
m
an
ag
em
en
t,
ex
te
rn
al
st
ak
eh
ol
de
rs
,a
nd
th
e
lik
e
D
is
cl
os
ed
,a
ud
it
co
nc
lu
si
on
D
is
cl
os
ed
,r
ep
or
t
to
m
an
ag
em
en
t
an
d
so
on
.
N
ot
di
sc
lo
se
d
A
ud
it
qu
al
it
y
In
fo
rm
at
io
n
re
la
te
d
to
hi
gh
er
au
di
t
qu
al
it
y
by
em
pl
oy
in
g
B
D
A
an
d
an
al
ys
in
g/
ch
ec
ki
ng
10
0
pe
r
ce
nt
of
co
rp
or
at
e
da
ta
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
D
is
cl
os
ed
w
it
h
an
or
ie
nt
at
io
n
to
w
ar
ds
th
e
fu
tu
re
C
on
tr
ol
of
au
di
t
qu
al
it
y
In
fo
rm
at
io
n
re
la
te
d
to
th
e
co
nt
ro
lo
fa
ud
it
qu
al
it
y
in
si
de
th
e
au
di
t
co
m
pa
ny
,a
s
w
el
la
s
ex
te
rn
al
pu
bl
ic
co
nt
ro
l
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
D
is
cl
os
ed
w
it
h
an
or
ie
nt
at
io
n
to
w
ar
ds
th
e
fu
tu
re
M
an
ag
em
en
t
In
fo
rm
at
io
n
re
la
te
d
to
im
pr
ov
em
en
ts
in
co
nt
ro
la
nd
de
ci
si
on
-m
ak
in
g
fu
nc
ti
on
s
by
us
in
g
B
D
an
d
B
D
A
N
ot
di
sc
lo
se
d
D
is
cl
os
ed
N
ot
di
sc
lo
se
d
(c
on
ti
nu
ed
)
Table III.
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34,7
760
O
pe
n
co
de
s
D
ef
in
it
io
n
A
ud
it
co
m
pa
ni
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co
m
pa
ni
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T
ax
an
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ud
it
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ud
it
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(b
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ig
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N
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di
sc
lo
se
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(c
on
ti
nu
ed
)
Table III.
Big data and
big data
analytics
761
O
pe
n
co
de
s
D
ef
in
it
io
n
A
ud
it
co
m
pa
ni
es
B
us
in
es
s
co
m
pa
ni
es
T
ax
an
d
au
di
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re
gu
la
to
rs
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te
rn
al
ca
pa
bi
lit
ie
s
In
fo
rm
at
io
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re
la
te
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to
th
e
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ti
vi
ti
es
,
ca
pa
bi
lit
ie
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an
d
in
te
rn
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pr
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s
ne
ed
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to
pr
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ar
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an
d
us
e/
an
al
ys
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D
in
a
co
m
pa
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ch
as
IT
w
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h
re
ga
rd
to
in
fr
as
tr
uc
tu
re
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is
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os
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D
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di
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cr
ea
si
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tr
en
d
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fo
rm
at
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th
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le
an
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of
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fo
r
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pu
rp
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co
m
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ni
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gl
ob
al
ly
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el
la
s
po
lit
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is
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du
ca
ti
on
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fo
rm
at
io
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re
la
ti
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to
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cr
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fo
r
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m
pe
te
nt
em
pl
oy
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s
w
it
h
bu
si
ne
ss
,I
T
an
d
m
at
he
m
at
ic
al
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m
pe
te
nc
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gl
ob
al
ly
D
is
cl
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ed
H
ig
h
im
po
rt
an
ce
D
is
cl
os
ed
N
o
te
s:
a D
is
cl
os
ed
m
ea
ns
th
at
th
e
op
en
co
de
w
as
m
en
ti
on
ed
an
d
di
sc
us
se
d
du
ri
ng
th
e
in
te
rv
ie
w
;b
no
t
di
sc
lo
se
d
m
ea
ns
th
at
th
e
op
en
co
de
w
as
no
t
m
en
ti
on
ed
or
di
sc
us
se
d
du
ri
ng
th
e
in
te
rv
ie
w
;c
hi
gh
im
po
rt
an
ce
m
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ns
th
at
th
e
op
en
co
de
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as
m
en
ti
on
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an
d
di
sc
us
se
d
ve
ry
st
ro
ng
ly
du
ri
ng
th
e
in
te
rv
ie
w
Table III.
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The results are presented from the different respondent groups’ points of view.
4.1 Audit companies
Current practices. Experience. Large audit companies (international networks) develop and
apply analytic tools that are similar to the BDA content-wise and complexity-wise. On
average, audit companies have applied modern analytic tools for two to four years in the
Baltic region. The auditors emphasise that the application of such innovative data analytics
in the Baltic region is actually not the first choice (as compared to the USA, the UK,
Germany or some Asian countries’ audit markets, for example). Big 4 auditors shared
similar practices:
We are a smaller country; therefore, we usually do not even get on the first wave of
implementation and application of innovative data analytics [Big 4 (2)].
However, some experts emphasised that companies had only taken the first steps in
analysing BD context, referring to the demand for BD-based tools:
We are making first steps but the practical implementation is not for today yet. [. . .] We are
developing applications, methodology. Some regions are more advanced, like North America, UK
or Asia. We [Lithuania] are more like recipients of innovations [Big 4 (1)].
Other experts confirmed that audit companies had already made a progress in developing
and applying analytical tools and had started to use the more advanced versions in
Lithuania:
[. . .] as we implement audit analytical tools very purposefully, now we develop and implement a
new and advanced analytical tool which was created and developed in UK office of our company
(International audit network).
Increasing trend. Conducting a BDA-based audit was a challenge for the auditors
themselves:
A possibility to audit all data is even now hardly perceivable for some auditors, as big companies’
audits are based on sampling methods. [. . .] With technologies, a huge amount of information in
an external audit does not play such an important role [Big 4 (1)].
Implementing BD technology-based tools establishes the conditions for changing the
thinking and attitudes of both auditors and business clients. In the case of a client being a
Table IV.
Axial codes derived
from second-order
codes
Second-order codes Description Axial codes
Current practices Arguments and descriptions related to the current
situation, experience and motivation to use BD/BDA
in companies
Current practices
Strategy-related company
factors
Different levels of the intensity of factors influencing
and motivating the level of BD/BDA use from the
internal environment of companies
Company factors
Resource-related company
factors
External factors Factors regulating, influencing and motivating the
level of BD/BDA use from the external environment
of companies
Institutional factors
Internal control The main areas that are influenced by the use of BD/
BDA in business and audit companies
Outcomes
Audit process
Quality
Big data and
big data
analytics
763
small business company, audit companies even have to show the value of using BDA in the
audit process:
We indicate the main advantages of using BDA for our small or new clients [such as] using BDA
we will be able to indicate the systemic problems and variances in your [business] company data,
increase the quality of audit report and to find the fraud events (International audit network).
Benefits. The largest audit companies (international networks) assessed the BD and BDA
unambiguously positively and treated them as a competitive advantage in the audit market
in the long term. Enabling auditing technologies will probably foster the competitiveness of
all audit companies in the oligopoly audit market:
[. . .] currently, analytics tools are used considerably more, as also our company itself has invested
a lot into these new analytics tools. We think that Big 4 (2) Eagle [analytical tool] is a competitive
advantage. [. . .] Unambiguously positive, as it helps to focus on riskier fields. It helps to identify
the fields that might look suspicious [Big 4 (2)].
Financial resources. Small audit companies usually only apply very simple analytical tools,
mainly because of lack of knowledge, poor financial resources and the cost of investment.
The current practices of small- and medium-sized national audit companies and audit
companies that belong to international networks strongly diverge with regard to applying
modern technologies:
[. . .] by investing in analytical tools we always measure costs [. . .] as it’s really very expensive
[Big 4 (3)].
[. . .] notwithstanding huge financial recourses needed, all investments are very useful. We
operate in a very competitive business environment where we have to make our processes more
efficient in order to compete with a lower price. [. . .] Technologies help to work efficiently and
save costs (International audit network).
The largest companies were usually more experienced in the use of data analytics and were
already gaining advantages because of the economy of scale.
Institutional factors. Regulation system. Institutional factors affect audit companies
themselves through the requirements for the performance of more efficient audits
(application of control tests and detailed procedures) and quality control. Hence, the
importance of ISA is evident. Audit companies also have an impact via the client, such as
additional legislative requirements for the quality of accounting and clients’ accounting IS.
If audited clients are small, their accounting IS will naturally be distinguished by a
smaller quantity of structured and non-structured data. The size of the client is also
associated with the fee for the audit. In fact, no companies in the Baltic region are big
globally; therefore, strong competition in terms of price is prevalent.
[. . .] clients are too small, because if we talk about analytical tools, we encounter limitations, one
of which is the size of the client, and then this is closely associated also with price limitations [Big
4 (2)].
Although Krahel and Titera (2015) and Vasarhelyi et al. (2015) argued that the application of
BDA would also bring about changes in ISA, audit experts did not think that auditing
standards and methods should necessarily change for the successful employment of these
analytic tools. Current legal acts are sufficient to conduct a BD-based audit:
Audit standards that have these requirements already require all companies to conduct an audit
in the most effective way using the analytics tools. This is simply another tool to achieve these
goals in a faster and better way. But this does not change the way that an audit team should
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work, what the work principles are, how we plan, organise, review and what the quality control is
[Big 4 (2)].
Standards are nevertheless a set of principles, not rules. As regards an understanding of the
company, control environment and all processes, it is already laid down in the standards that you
have to understand all processes, irrespective of whether you will subsequently validate the
control or not, and whether you are going to trust them [Big 4 (4)].
Thus, auditing standards are focussed on the audit’s purpose and general principles, not on
the techniques/analytics that are used to perform it.
Market structure. It is important to note that the market orientation of client’s company
may also determine the use of BD technologies and the market’s size:
Lithuania is not a big market size. If companies are just orientated to the Lithuanian market, it is
not large enough. They do not require substantial systems that would work with crazy amounts
of data. [. . .] On the other hand, more and more service centres are being established in Lithuania
[banks, sharing centres (explanation added)]. . . . The driver would be management established in
a foreign country [Big 4 (1)].
Education. One of the most important aspects when attempting to apply BDA successfully
is having competent employees. Education plays a critical role in providing audit specialists
with interdisciplinary competence:
[. . .] even the universities themselves should focus more on IT by preparing specialists. It is a big
challenge for us. We can see IT specialists who do not care anything about accounting, and
graduated accountants who have poor skills in IT. Unfortunately, we do not see the merger. [. . .]
So we are already looking for people with integrated skills [Big 4 (1)].
By developing and implementing BDA we saw the transformation in the audit profession and it’s
not enough to be only an accountant or auditor but we also need to have IT competences. . .
(International audit network).
As requirements for external auditor’s professional competence are set by public authorities,
there may be inevitable changes in the long run.
Company factors. Strategy-related factors. The use of modern analytics in large network
audit companies, including international audit networks, is based on the global strategy of
IT innovations:
No large companies stand still, and, talking about our company, this is a really global
network investing in these technologies. [. . .] there exists a common global strategy and a
vision of the company, when we all [units in different regions] will start using a particular
analytics tool [Big 4 (2)].
To be a part of a global business and to belong to international networks, plays an
important role in using BD in external auditing and the client’s performance:
Most of the businesses, especially IT businesses, are foreign owned. They are driven by a parent
company. [. . .] So, the ownership structure is an important factor [Big 4 (1)].
The motivation of audit companies to invest in analytics tools relies primarily on the size of
the company and its strategic orientation. International audit networks and large audit
companies have greater possibilities of creating or acquiring such powerful analytics tools:
We do not develop such analytics tools in the Lithuanian unit. We use what has been globally
created in the company [Big 4 (2)].
Big data and
big data
analytics
765
Notably, large audit companies (such as the Big 4) see BD as an increasingly essential part
of their assurance practice (Alles and Gray, 2016). It is important to note that the size of the
company determines the use of BD technologies not only due to the size of the audit
company itself but also based on the size of the audit client. The business client’s size was
one the most prevalent factors mentioned by the experts who were surveyed. If business
companies are small, their data are naturally not defined by the characteristics of 3Vs. This
theoretical presumption is consistent with the answers from regulators and auditors:
Multinational companies are big drivers. Facebook and Google are driving the auditors’
profession as well. We have to find ways to audit them and Big Data Analytics may help
[Big 4 (1)].
The size of a company can have an influence on the use of BD from the point of view of the
amount of data and probably in the future, even medium-sized companies will be able to apply
and use it (Global financial services and IT company).
Resource-related factors. Audit companies have to be prepared in terms of their internal
processes and capabilities to use BDA. They mainly need resources related to the
preparation of IS and integrated teams of employees for the successful application of BD and
BDA. As IT competencies are becoming extremely important, audit companies currently
resolve this issue by having an IT person in the company or outsourcing IT competence:
[. . .] We know what we want but we do not have IT competencies, so it’s better to take from
software companies. We are talking about major software companies like Microsoft, Oracle, SAP.
Obviously, the cooperation with these companies will help to develop the tools [Big 4 (1)].
We have an IT person who works with different groups and consult about IT questions [Big 4 (4)].
Outcomes. Audit process. For audit companies, BD may help to provide a better
understanding of the business client’s environment. All the experts interviewed claimed that
the application of these analytic tools made the audit process more effective, particularly
during the phase of understanding the client’s business environment and internal control
and during the phase of performing substantive procedures:
The reasons to perform an audit are more focused on risks, conduct it in a better, quality manner,
adapt to progress [Big 4 (2)].
Effectiveness is at the first place as competition by prices is essential. We are working totally in
electronic space [Big 4 (3)].
[. . .] our analytics show a certain tendency and variances in, for example, your [business client]
company and you [business client] are able to analyse detailed data where and why it [variances]
were found (International audit network).
An audit company, as a profit-seeking organisation, seeks to conduct an audit in the most
efficient way from the client’s and the quality point of view. Thus, analytic tools are one of
the instruments that reduce the screening risk, and thus, minimise the likelihood of incorrect
conclusions. Essential attention in the BD-based audit is paid to the verification of data
reliability. This is irrespective of whether the client’s information would be received in the
traditional way or via BDA; the issue of data reliability is always a priority:
The first work upon receipt of any information for auditing purposes is a test of its reliability.
[. . .] The main question during the verification of quality control is whether a data reliability test
has been made [Big 4 (4)].
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A set of BDA tools may also be beneficial for the drawing up of audit reports. During the
auditing process, co-operation is maintained with the company’s management, and different
reports may be drawn up (such as the auditor’s conclusion, the auditor’s report and letters to
the management). The final auditor’s conclusion is standardised, with clear criteria for the
information provided. Therefore, the BDA may have an indirect effect through the type of
auditor’s opinion. In other words, when applying more effective analytic tools, the
assumption is that the auditor had a better perception of the client’s environment, focussed
accordingly on the riskiest fields and decreased the likelihood of having provided an
incorrect opinion.
However, the possibility of using analytic technologies in other audit reports is much
greater and may create more added value for the client, only without the compulsory
compliance function:
A letter to the management where we share observations on internal control systems, their
shortcomings, provide recommendations that do not necessarily impede an audit, but we simply
share our insights. Thus, here we see very great possibilities that namely in this place [assessment
of the internal control system] the use of BDA would be of great help because [. . .] it would be an
analytics in different cross-sections [Big 4 (4)].
Quality. An audit market regulator and quality control may also be very important factors
fostering BDA in external audits. State regulation of the audit market is gradually growing
stronger across the world (SOX, Audit directives in the European Union, etc.). Thus, there is
noticeable pressure from individual audit quality regulators to apply more advanced
analytic tools in the audit process, which would translate into a better quality of risk-based
audits:
The need to apply advanced analytics tools arose not only from the audit teams themselves but
also from the quality control system. [. . .] An American regulator treats quality control systems
of audit companies extremely strictly and its audits are substantial. This is also the second strict-
wise and attitude-wise regulator in the Netherlands [Big 4 (4)].
Institutional quality control factors of external audit companies via the audit market
regulators in different markets produced a different effect:
Maybe, if we were only a national company and with this regulator, then we would probably have
less boost, but in fact, our global methodology team is in America and they work in the strongest
professional regulation environment. Thus, all approaches, all innovations, novelties and pressure
on the maintenance of audit quality come from over there [Big 4 (4)].
This is an approach of the global body that regulates all this audit policy [Big 4 (1)].
Internal control. When public interest companies are audited, the use of these tools becomes
an essential element for assessing the control system and managing the audit risk:
[. . .] one of our tools makes a very good report from the accountancy data, which makes it clear
whether a person has made any entries he cannot make and whether the duties are separated,
whether one and the same person does not do both, debit and credit, as this entails an additional
risk [Big 4 (2)].
Thus, there is a need for tools that would enable conducting an audit in an effective way, that
would enable to conduct it in a faster and better way, as quality may not be compromised either,
and the audit standards themselves, as I have mentioned, become not looser, but more stringent
[Big 4 (2)].
Big data and
big data
analytics
767
Estimation of a client’s internal control system is one of the compulsory analytical
procedures for an auditor. The more complex and global the client company is, the more
multidimensional and complex is the internal control system of the client.
Issues related to the audit company. According to the research results, all second-order
codes were disclosed in the case of an audit company, and this could be explained as all
contingent factors influenced the use of BD and BDA, but the influence occurred at
different levels and degrees of importance. Our research results suggest that the use of
BD and BDA depends strongly on the audit corporate strategy and governance structure
and it confirms the research results of Verma and Bhattacharyya (2017). Moreover, it is
likely that BDA enables auditors to act on structured and unstructured information. In
line with Bhimani and Wilcocks (2014), we claim that the traditionally presumed
sequential and linear links among corporate strategy, governance structure and IS design
are no longer in play. This is the reason that we also suggest that, when applying the
BDA, additional attention should be paid to the company’s IS as one of the elements of the
internal control system. To a great extent, the IS depends on whether the auditor will be
inclined to trust the data or to apply more detailed audit procedures. The issue of the
reliability of the IS is crucial. Our study also suggests that the development of new
analytical competence and even a new structure of audit teams with regard to BDA is
necessary. In line with Al-Htaybat and Alhtaybat’s (2017) views on BD in corporate
reporting, building such teams (that include analytics) will require audit companies to
determine whether they want to outsource their analytics or whether they want to create
their own platforms and systems.
4.2 Business clients
Current practices. Experience and increasing trend. The use of BD and DA tools in business
companies (including international companies) is already the practice, with more than five
years of history and a trend towards expanded use in the future:
Banking sector was especially in a very good situation concerning BD because of regulation to
collect and save historical data. Analytics was just the next natural step forward (Financial
institution operating worldwide).
The implementation of BD technology-based tools establishes the conditions for changing
the thinking and attitudes of business companies:
BD is a global trend, everybody [business companies] can see and understand the value of using
BD and this understanding has become comprehensible to owners of businesses (Financial
institution operating worldwide).
Benefits. Business companies see BD and DA as an essential process in today’s business
environment and use them for a different purposes and benefits in areas such as cost saving,
planning processes, forecasting of the client’s behaviour and sales:
[. . .] there are a lot of areas where labour work could be changed with analytic [. . .] to predict the
client behaviour is one the possible usage of BD and another could be after-sale service (Financial
institution operating worldwide).
[. . .] each business unit has its own data analytics in different levels, such as risks, fraud, pricing,
transaction analytics, accounting analytics, marketing analytics (Global financial services and IT
company)
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Financial resources. Business companies see the implementation and use of BDA as a
process that is expensive and which requires a financial investment. The influence of this
concept is that it is mainly large companies that are able to integrate and use BDA widely:
[. . .] from practical point of view, there are a small number of companies in Lithuania, which
could be able to use it [BDA]. It is understandable that you [Business Company] cannot expect
results from BD in six months, it is quite a long period and company has to understand this, you
have to invest and work (Financial institution operating worldwide).
Institutional factors. Regulation system. The sector regulator (such as the financial sector)
and the audit regulator play an important roles in the use of BDA:
[. . .] financial institutions historically must accumulate and save a different kind of data to
manage risk issues (Financial institution operating worldwide).
The audit regulator should encourage audit companies to be more advanced technologically, to
provide fresh news about novel audit analytics. Such topics are not even included in annual
training for auditors (National audit network company).
Market structure. The main motivating factors for using BD in business companies are
strong competition and long-term relationships with customers. Many interviewees
emphasised:
The main motivating factor is to create a sustainable relationship with customers (Financial
institution operating worldwide).
Competition is very strong in the market and a company needs to be better than its competitors,
so BD helps to ensure this aspect (Global financial services and IT company).
Education. These global trends influence the need for employees with broader interdisciplinary
competence, including knowledge about business, information technology and mathematics.
Business companies confirmed the importance and lack of competent employees globally:
[. . .] companies are lacking competent employees and looking for them, . . . it is very difficult to
find employees who would be ready to work in BDA area and even with experience (Financial
institution operating worldwide).
[.] there is an increasing level of interest from universities and study programmes but we still are
not able to find a fully prepared specialist able to work with BD. Mostly cases we invest in
competences improvement of those employees who have IT, mathematical or analytical skills
(Global financial services and IT company).
Company factors. Strategy-related factors. From the client’s perspective, the use of BDA and
DA rely heavily on the corporate strategy and top management’s support:
The main objective of all financial institutions operating worldwide group is BD integration into
business processes with purposes to minimise costs and to discover new possibilities for business
development (Financial institution operating worldwide).
[. . .] as changes are very fast in the market, decisions made have to be grounded by BD and
according to strategic choice of all company groups in all Europe and this is not limited to the
Lithuanian market (Global financial services and IT company).
Resource-related factors. Large companies will be more financially able to invest in new
technologies and capabilities (infrastructure and competent employees) and to invest in the
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future value that could be created by BD. In addition, it could be stated that companies in
developing countries might be able to integrate BDA more quickly:
[. . .] because banking companies already started to develop business with more recent
information technologies and systems that allow to integrate BDA and to be more flexible
(Financial institution operating worldwide).
The main challenges for the application of BD in external auditing are the quality and
comparability of data and qualified BD analysts because companies need to have employees
who can find patterns in data and translate them into useful business information:
BD quality is very important . . . [. . .] We have two groups of BD, first is more raw data and using
it is allowed but risks need to be evaluated, second is fully prepared BD (Financial institution
operating worldwide).
The main internal challenge of using BD is HR and analytical skills integrating IT and business
skills. [. . .] Also, one more challenge is IT system and necessary investments into these systems,
consultancies (Financial institution operating worldwide).
Outcomes. Internal control. Business companies understand BD as the possible or the main
source of data to manage the business and use BDA tools for internal management, decision-
making, planning and reporting purposes:
We use BD in weekly control process by evaluating changes, influences and making decisions.
[. . .] Our expectations are that BD application will grow in the area of business process
development in the future. (Global financial services and IT company).
Issues related to business clients. The research results showed that not all second-order
codes were indicated in the case of business companies. In particular, the difference from
audit companies was in the area of outcomes. This could be explained by the fact that
business companies mainly use BD information for internal purposes to manage business
processes and make decisions. The research results confirmed that the possibility of
applying BD and BDA depended on the size of the business company and its strategic
orientation. Public interest companies, companies with international headquarters in
different countries, may encounter actual BD in their activities. The motivation to use
BDA and other DA is also important regardless of whether the client is a state-owned
company or a private company. The main motivation to use BD and BDA tools is related
to strong competition.
4.3 Regulator
Current practices. Increasing trend. Regulatory bodies understand the importance of BD/
BDA tools and see them as an increasing trend for all sectors, business companies, audit
companies and as a future direction in the case of regulatory bodies as these still do not have
experience in this area:
[. . .] our performance is very closely related with BD technologies. [. . .] because of looking at the
future all large business companies will need to provide all information to regulating
governmental institutions in electronic form starting from 2017 (Tax analytics).
Benefits. Regulatory bodies confirmed the usefulness of BD and BDA for large business
companies, governmental organisations and at the state level from the perspectives of time
and quality:
It [analytics tool] shows directions where mistakes, irregularities might be (Tax analytics).
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[. . .] this was the initiative from business companies. As The State Tax Inspectorate disrupted
companies with questions about different kind of data for two weeks, so it [BDA] is a benefit for
both parts (State Tax Inspectorate).
Financial resources. According to the experts interviewed, there is a need for e-audits and for
a funding project to support the implementation of e-audits, which will help to develop and
use BD-based analytic tools for different purposes:
There should be some actions taken and start a project implementation in a three-year period
(State Tax Inspectorate).
Cost benefit aspect is very important and we calculate the employees’ time saved for different
processes from regulator and business company sides, this helps to evaluate money saved in five
years, ten years or fifteen years (Tax analytics).
Institutional factors. Regulation system. Regulatory bodies play an important role at various
levels, such as in the tax environment, and in terms of sector regulation and audit regulation.
In the global regulation practice, it is still possible to notice different variants, ranging from
the compulsory universal certification of accounting systems to plans to certify accounting
information provided by companies:
Accounting systems are certified at the state level. [. . .] the same way an accountant must have a
certificate, an IS must be certified. [. . .] The future will unambiguously have to be this way, as the
number of errors due to low-quality information will make the process very painful (State Tax
Inspectorate).
According to the experts interviewed, one of the factors motivating the use of BDA will
definitely be the fostering of e-audits at the state level:
It is very important to make a breakthrough in the analytics, an audit breakthrough, a quality
leap so that we could audit banks not in the way we audited Snoras or U°kio bank. Positive audit
reports were issued and in a half-year, these banks became insolvent (explanation added) (State Tax
Inspectorate).
Education. Regulatory bodies indicated the future need to integrate educational institutions
in this increasing trend towards BD and BDA:
We plan to integrate researchers in the development of analytical tools. [. . .] there is still a lack of
knowledge and wisdom about the same understanding. Education would be able to play a key
role in this process (State Tax Inspectorate).
Outcomes. Audit process. Obviously, audit regulatory bodies do not participate directly in
the audit process, but their key function is the public oversight of quality control.
Responsible regulatory bodies evaluate how audit evidence is documented and the
compliance with ISA and the completeness of substantial audit procedures and control tests,
including audit evidence gathered via BD:
If transactions and accounting records are maintained in a ecentralizat way, a large company may
simply face the fact that data are wrong. Overall, the system seems to be correct, but
decentralization may show that, with time, these data have changed. This may be a big surprise
for such large companies [Regulator (2)].
Quality. As Lithuania abandoned national auditing standards in 2009, the Lithuanian audit
regulator does not have sufficient authority to change the implementation of the standards.
It is not the standard setter and has more of an advisory role:
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So, the biggest driver comes from international accounting settlers. [. . .] For the more advanced
regulators in Europe and other territories it is the tendency. As auditors, we move to a more
sophisticated IT environment of auditing the clients. The regulators have to understand how the
auditors audit. It might be even the beginning of the process [Big 4 (1)].
Internal control. Essentially, ISA lays down the provisions for assessing the client’s internal
control system, the IS and controls regarding the IS:
There are many different types of accounting software and auditors are familiar with some and
not familiar with others (Tax analytics).
The possibility of checking data in real time results in the likelihood that an audit may
create a higher value for the client. This would not only be an auditing process based on
historical data:
The reaction to on-going processes and the speed are very important. Now auditors make a
sampling and audit the data that is half-a-year, one-year old. [. . .] Thus, this reaction in current
time and controlling such data is very important to be able to react in a fast and expeditious
manner (Tax analytics).
Overall, auditors and regulators presented a conservative attitude towards incorporating
BD in decision-making for auditing aims. They admitted that BD played an important role,
but that the change will still be taking place in the future.
Regulator-related issues. The research results showed that second-order codes were
disclosed differently in the case of regulators. Company-related factors were not disclosed
because regulatory bodies are not treated in the same way as are companies. Regulatory
bodies still do not have current practice in the use of BD and BDA tools and the
implementation, thereof, is planned for the future. Institutional factors were disclosed
because regulatory bodies play an important role at various levels, such as in the tax
environment, in sector regulation and audit public oversight. Outcomes were mainly
disclosed with regard to quality, and this could be explained by the fact that regulatory
bodies are responsible for the public oversight of quality control, continuous learning and
education about innovative audit techniques, including BD and BDA. According to the
research results, regulatory bodies could be seen as followers of business and audit
companies in the use of BD and BDA tools.
5. Discussion and conclusion
5.1 Comparison and discussion of the results
Based on the qualitative research, we identified four key results. By disclosing a
comprehensive view of current practices (one), we identified two groups of motivating factors
[company-related (two) and institutional (three)] for the use of BDA from an external auditing
point of view, which may lead to the desired outcomes (four) for the audit companies.
Our findings showed that the current practice differed for business companies, audit
companies and regulators. Business companies had used BDA tools for more than five years
and saw this as an increasing trend in the future because of strong competition, and these
tools were used to understand the customers’ behaviour, to manage risk and for internal
management purposes. Hence, the use of BD and BDA was focussed mainly on the internal
management needs and market/sales expectations. Audit companies had approximately
three years of experience in the use of BDA tools. The use of modern analytics in large
network audit companies was usually based on the global strategy of IT innovations and
with the main purpose of ensuring the quality of the audit process and to issue a relevant
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auditor’s report. Regulatory bodies still did not have experience in the use of BD and BDA
tools and assume this would be an increasing trend in the future.
Our study, therefore, emphasises the importance of interdependence among audit
companies, business clients and regulators to enable the use of BD and BDA. Given this,
business companies might be the drivers of the use of BD and BDA tools and audit
companies might adopt these innovations because of high competition in the audit market.
Moreover, the current practices of business companies provided and even created suitable
conditions for external audit companies to use all the data (financial and non-financial,
structured and unstructured) for audit purposes. This motivates external audit companies to
use BDA as, firstly, business companies are able to provide BD and, secondly, the use of
BDA for audit purposes allows the achievement of the desired outcomes, such as the
efficiency and effectiveness of the audit, higher audit quality and minimising audit risk and
having a better understanding of the client’s business environment and internal control.
Specifically, the study has provided evidence of the importance of motivating factors and
circumstances that influence the use of BDA in external auditing process (Table V).
The results from the interviews showed that contingent factors may act both on the
company level (such as size, strategic orientation, structure and technology) and on the
institutional/external level (the audit market environment). What is more important is that
the influence of different contingent factors was not the same. Company-related factors had
a direct influence on the use of BDA in different phases of the audit, depending primarily on
the audit company’s data-driven strategy and the business client’s size. Moreover, the audit
market environment (the national regulator’s policy or the competition level) could be
assumed to be an indirect contingency factor because audit companies have to evaluate
environmental uncertainty and adapt to it.
Our findings showed that a company factor such as size influenced the use of BDA for both
audit companies and clients. These results are in contrast to the study by Li et al. (2018), who
found that corporate size did not influence the adoption of audit analytics in internal auditing
significantly. One reason could be that, if the audit client is extremely large, the client will be
confronted with plenty of semi-structured and unstructured massive data that cannot be
analysed using traditional audit software and analytics. On the other hand, only a large audit
company may have sufficient resources and substantial tools to be able to audit such a
company. This is also consistent with previous research stating that large companies have
extensive specialisation, standardisation and formalisation (Wickramasinghe and Alawattage,
2007), while small companies will not be able to provide all the necessary information as BD. In
addition, a small audit company would encounter challenges when attempting to use BDA
because of the lack of trained staff and technological capability (Alles, 2015).
With regard to the strategic orientation, our results are consistent with those of Li et al.
(2018) and Verma and Bhattacharyya’s (2017) findings that the major reason for the non-
adoption of BDA was that companies did not realise the strategic value of BDA, and they
were not ready to make changes due to technological, organisational and environmental
difficulties. Therefore, we conclude that a company’s strategic orientation and structure may
also be important influential factors concerning the use of BDA. On the other hand,
competent employees, internal capabilities and IS are resource-related audit company
factors because they are derived from the size of the company and from the strategic
orientation/attitude towards the adoption of technology. Moreover, audit companies attempt
to find a trade-off between the extent of information demanded by the environment and the
company’s available resources.
Audit market regulations and education may have a particular impact on an audit
company’s decision regarding the design of an audit strategy, such as how to apply modern
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auditing tools, how to ensure audit quality and what the topics for auditors’ training should
be. Our results are in line with Tarek et al. (2017) and Li et al. (2018), confirming that the
attitudes of audit regulatory bodies and legislative regulation followed by sector regulation
and market structure are critical for fostering the use of BDA.
Specifically, we provide the following theoretical and practical implications:
� Our paper expands on Li et al.’s (2018) study on understanding the use of audit
analytics for internal auditors due to several reasons. We aimed to investigate practices
pertaining to the use of BDA, in particular, (not all audit analytics in general) in external
auditing. Although external and internal auditors have similarities in terms of carrying
out audit procedures, the role of external auditors of decreasing information asymmetry
for capital markets is distinct and unique when compared to internal auditors.
Furthermore, external auditors must be independent and do not participate in an
Table V.
The highlights of
motivating factors
and circumstances
Motivating factors Motivating circumstances
Company-related
Size
Audit company’s size Audit companies with large international audit networks have more
capacity
Business client’s size Large business clients may have more BD
Strategic orientation
Data-driven strategy Data-driven strategy of the audit company
Client’s selected business
model
Usually business to consumer (B2C) experience more BD
Relationship between the
audit company and
business clients
In the case of a long-term contract, additional costs for initial
harmonisation and the correlation of different data sources
Structure
Audit company’s
structure
Global audit networks
Business client’s
ownership structure
In the case of a business company, public procurement has to be organised
for a state-owned company and, in most cases, only for one year
Sector Specific sectors in which BD is inherent, such as financial intermediation or
telecommunications
Technology
Digitalisation of the
business process
High degree of IT usage by audit companies and business clients
Accounting software used
by business clients
Technological level of accounting software. Usually BDA are not well
adapted for working with national accounting software, as there are
particular difficulties such as the extraction of data in the necessary
format, and initial processing to receive such data
Professionals with BDA
experience
Member of audit team/ outsourced professional/internal training
Institutional
Audit market
environment
Audit market competition High audit market competition. Strong price competition is prevalent in the
Baltic region
National audit regulator’s
policy
Help/support to acquire BDA or AA, provide training about analytics in
auditing
Education Higher education institutions to provide professionals with
interdisciplinary data analytic skills
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audited company’s activity constantly, as internal auditors do. This means that external
auditors have to gain understanding of the client’s environment and performance in a
very short time; hence, BDA might be a useful tool. While Li et al. (2018) emphasised
that only internal auditors should have more demand for the use of audit analytics to be
efficient and effective, the high prices and competition in the external audit market are
very important factors motivating the need to be more effective and implementing
more analytics. From the interviews, we may summarise that audit clients seek: to
negotiate for better pricing because of high competition in the audit market; and to get
more value and insights about corporate risks and performance. This leads to a trend
whereby external auditors are likely to focus on the procedures not just to satisfy
regulatory requirements, but to provide more value for the audit client; hence, BDA
may be one of the solutions.
� The results of our research also indicated diverse motivation in the use of BDA
depending on the business client’s size. Large business companies usually acted as
innovators in applying BD and audit companies were followers. In the case of the
client being a small business company, audit companies played a proactive role and
even had to demonstrate the value of using BDA in the audit process.
� The result that the national audit regulator was lagging behind in implementing
audit analytics was particularly problematic from a BD and BDA perspective. In
most cases, the national audit regulator played more of an advisory role, and was
currently lagging behind with regard to BD and BDA. From this perspective, the
study also outlined the dilemma of quality. Audit regulators need to ensure public
oversight of quality control and provide training for auditors. However, regulators
lacked knowledge about innovative BD-based techniques.
5.2 Conclusion and further research directions
The results of our research revealed audit companies’ intentions to use BDA and to expand
their understanding of the use of BD and BDA tools in external audits by emphasising the
close relationship of audit companies and different; yet, related groups such as business
clients and regulatory bodies.
We wish to emphasise the need to implement BD and BDA-based audit practices for
audit companies as a way to improve audit quality and to foster the efficiency of audits,
which may result in a competitive audit fee. This research also offers insights into helping to
customise their audit strategies.
In addition, our research results indicated that large business clients were the main drivers
of the use of BD and BDA in external auditing, as the current practices of large business
companies allow and create suitable conditions for audit companies to use BD (financial and
non-financial, structured and unstructured) for audit purposes. Large business clients usually
act as innovators in applying BD and BDA, while audit companies are followers. However, a
different direction in this relationship could be indicated in the case of small business clients, as
audit companies play a proactive role in this scenario and even have to show the additional
value of using BDA. Moreover, based on the interviews, we suggest that large networking
audit companies may gain long-term effectivity, which is important regardless of whether the
client is new or established. The other outcome is to ensure a higher audit quality resulting in
better value for the shareholders, the management and society.
For business clients and regulators, this study might help them to understand the
advantages and challenges of institutional and company factors concerning BDA use.
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5.3 Contribution
Our study aims to contribute to the literature on auditing in the following ways. Firstly, it
adds to the small body of research by offering an empirical investigation the state-of-the-art
of BDA usage and motivating factors in external auditing. While prior studies (Li et al.,
2018) have focussed on internal auditing, this paper addresses BDA and external auditors in
particular. In addition, Verma and Bhattacharyya (2017) found that complexity and
perceived costs were the inhibitors that prevented the adoption of BDA in business
companies, while our research results indicated that the factors mentioned above were not
critical. Secondly, our study examines the phenomenon of BD and BDA in the context of
auditing. It is important to note that BD has specific characteristics compared to other types
of data and opportunities to use BD within BDA is of increasing importance for audit
companies, which to the authors’ knowledge, is absolutely new. Structured (around 10 per
cent) and unstructured (around 90 per cent) of data that are large in size cannot be analysed
using traditional software and database systems (Al-Htaybat and Alberti-Alhtaybat, 2017).
Thirdly, the paper presents a contingency-based theoretical framework as a model
explaining how different motivating factors may influence the use of BDA. The research
also makes a methodological contribution by using the approach of constructivist grounded
theory for the analysis of qualitative data.
5.4 Limitations
The conclusions of this study are based on interview data collected from 21 participants.
Future studies may investigate the issues addressed in this study further by using different
research sites and a broader range of data. Although the theoretical method is highly
transparent, it requires further testing to verify the mechanism on which it is based.
Furthermore, by keeping BDA as a tool, the use of which depends on the size of the company,
our sample yielded all interviews in particularly large companies. There is a limited number of
large companies in Lithuania that are open to co-operation. To test our research question more
broadly, we suggest including additional audit and business companies in future research.
5.5 Future research
There are a number of future research opportunities, as this is still a novel research area in the
field of auditing and accounting. Having chosen a qualitative approach prevents a broader data
collection method, which may provide different views. It would be worthwhile to carry out
further empirical analyses of BDA either currently or potentially in use through a detailed case
study or a quantitative survey to gather a broader range of insights. Our interview results
provided mixed results with regard to the need to change auditing standards and auditing
procedures when using BD. Thus, a deeper discussion of possible changes to audit procedures
could be another relevant area for future research. As we identified that the national audit
regulator is currently lagging behind in the area of audit analytics, it would be relevant to
investigate the quality dilemma from the perspective of public oversight of quality control and
the impact of international and national audit regulators on BDA and audit analytics in general.
Furthermore, it is worth conducting research on changes in external auditors’ profession through
education in analytical interdisciplinary skills. At the same time, future research could expand the
scope of BD and BDA research for the internal purposes of companies, such as internal auditing,
control processes and performance measurement. The interviewed experts confirmed the
importance of BD usage for the management of pricing, fraud detection, complaints and risk
assessment. Performance measurement integrated with BD would be able to support planning,
control and decision-making processes by providing meaningful and appropriate information.
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Appendix
Table AI.
Interview guide
Questions to ensure
maintenance Enquiries
Why do you (not) use Big Data Analytics?
What is the motivation
behind this decision?
What is the corporate strategy regarding the use of modern data
analytics (Big 4)?
How long has the company been using Big Data Analytics and other
data analytic tools?
What are the benefits/costs of Big Data Analytics?
What internal factors drive your company to use Big Data Analytics?
What are internal factors
influencing the use of Big
Data Analytics?
What is the influence of the company’s size and the client’s size?
What is the influence on the auditing process in terms of:
Understanding the client and its environment,
Audit planning,
Sampling methods,
Other auditing techniques,
Auditing conclusion/reports?
What external factors drive your company to use Big Data Analytics?
Has external pressure
influenced the use of Big
Data Analytics?
What is the influence of the national regulative body?
What is the influence of the audit market’s size/competitors?
Which external groups - competitors, clients and other regulative
authorities have the biggest influence on the use of Big Data
Analytics?
How is (or how could) Big Data Analytics be implemented in the auditing process?
Who is involved in the
process of Big Data
Analytics?
Who prepares the Big Data? Who analyses the Big Data?
How do Big Data Analytics help to integrate non-traditional sources of
data with financial data?
How did your company create and implement Big Data Analytics?
Who created the Big Data
Analytics tools?
Do you use the services of IT consultancy companies?
Do you use your own capabilities?
Which changes do you expect in auditing?
Do you think Big Data
Analytics is a growing
trend?
Do you expect any
changes in the regulatory
framework?
What changes could there be concerning auditors’ competence?
Could there be a change from sample-based auditing to continuous
auditing?
What changes could there be for professional and educational
institutions?
Big data and
big data
analytics
781
About the authors
Dr. Lina Dagilien_e is a Professor at School of Economics and Business in Kaunas University of
Technology, Lithuania. Her research interests include sustainability accounting and reporting,
financial accounting and auditing issues. She is also interested in interdisciplinary projects due to
accounting sciences and is a developer of interdisciplinary graduate study programme “Business Big
Data”. Lina Dagilien_e is the corresponding author and can be contacted at: lina.dagiliene@ktu.lt
Dr. Lina Klovien_e is an Associate Professor at School of Economics and Business in Kaunas
University of Technology, Lithuania. She joined Kaunas University of Technology in 2012, before she
worked in a business company (Scandinavian capital bank in Lithuania) for nearly 8 years. Her main
research interests include the intersection of performance measurement/management control systems
and innovations.
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Motivation to use big data and big data analytics in external auditing
1. Introduction
2. Literature review and theoretical framework
2.1 Literature review of big data analytics in external auditing
2.2 The theoretical framework
3. Research methodology
3.1 Data collection
3.2 The setting of the Lithuanian audit market
3.3 Coding and analyses
4. Results and findings
4.1 Audit companies
4.2 Business clients
4.3 Regulator
5. Discussion and conclusion
5.1 Comparison and discussion of the results
5.2 Conclusion and further research directions
5.3 Contribution
5.4 Limitations
5.5 Future research
References